首页 \ 问答 \ Apache火花文本相似度(Apache spark text similarity)

Apache火花文本相似度(Apache spark text similarity)

 我在java中尝试以下示例 
 

 Apache Spark中的高效字符串匹配 
 

 这是我的代码 
 

public class App {
    public static void main(String[] args) {
        System.out.println("Hello World!");

        System.setProperty("hadoop.home.dir", "D:\\del");

        List<MyRecord> firstRow = new ArrayList<MyRecord>();
        firstRow.add(new App().new MyRecord("1", "Love is blind"));

        List<MyRecord> secondRow = new ArrayList<MyRecord>();
        secondRow.add(new App().new MyRecord("1", "Luv is blind"));

        SparkSession spark = SparkSession.builder().appName("LSHExample").config("spark.master", "local")
                .getOrCreate();

        Dataset firstDataFrame = spark.createDataFrame(firstRow, MyRecord.class);
        Dataset secondDataFrame = spark.createDataFrame(secondRow, MyRecord.class);

        firstDataFrame.show(20, false);
        secondDataFrame.show(20, false);

        RegexTokenizer regexTokenizer = new RegexTokenizer().setInputCol("text").setOutputCol("words")
                .setPattern("\\W");
        NGram ngramTransformer = new NGram().setN(3).setInputCol("words").setOutputCol("ngrams");
        HashingTF hashingTF = new HashingTF().setInputCol("ngrams").setOutputCol("vectors");
        MinHashLSH minHashLSH = new MinHashLSH().setInputCol("vectors").setOutputCol("lsh");

        Pipeline pipeline = new Pipeline()
        .setStages(new PipelineStage[] { regexTokenizer, ngramTransformer, hashingTF, minHashLSH });

        PipelineModel model = pipeline.fit(firstDataFrame);

        Dataset dataset1 = model.transform(firstDataFrame);
        dataset1.show(20,false);

        Dataset dataset2 = model.transform(secondDataFrame);
        dataset2 .show(20,false);

        Transformer[] transformers = model.stages();
        MinHashLSHModel temp = (MinHashLSHModel) transformers[transformers.length - 1];
        temp.approxSimilarityJoin(dataset1, dataset2, 0.01).show(20,false);

    }

    protected class MyRecord {
        private String id;
        private String text;

        private MyRecord(String id, String text) {
            this.id = id;
            this.text = text;
        }

        public String getId() {
            return id;
        }

        public String getText() {
            return text;
        }

    }

}

 

 在调用approxSimilarityJoin之前，两个数据集如下所示。 
 

 转换数据集A. 
 

+---+-------------+-----------------+---------------+-----------------------+----------------+
|id |text         |words            |ngrams         |vectors                |lsh             |
+---+-------------+-----------------+---------------+-----------------------+----------------+
|1  |Love is blind|[love, is, blind]|[love is blind]|(262144,[243005],[1.0])|[[2.02034596E9]]|
+---+-------------+-----------------+---------------+-----------------------+----------------+

 

 转换数据集B. 
 

+---+------------+----------------+--------------+----------------------+----------------+
|id |text        |words           |ngrams        |vectors               |lsh             |
+---+------------+----------------+--------------+----------------------+----------------+
|2  |Luv is blind|[luv, is, blind]|[luv is blind]|(262144,[57733],[1.0])|[[7.79808048E8]]|
+---+------------+----------------+--------------+----------------------+----------------+

 

 虽然两个文本“爱是盲目的”和“Luv是盲目的”几乎相似，但我得到以下空白输出。 
 

+--------+--------+-------+
|datasetA|datasetB|distCol|
+--------+--------+-------+
+--------+--------+-------+

 

 请退回如果上述代码中有任何错误。 
 

 我通过为两个数据集提供相同的输入进行测试，下面是输出。 当两个数据集具有相同的文本时，distCol为零。 
 

+--------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------+-------+
|datasetA                                                                                                                        |datasetB                                                                                                                        |distCol|
+--------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------+-------+
|[1,Love is blind,WrappedArray(love, is, blind),WrappedArray(love is blind),(262144,[243005],[1.0]),WrappedArray([2.02034596E9])]|[2,Love is blind,WrappedArray(love, is, blind),WrappedArray(love is blind),(262144,[243005],[1.0]),WrappedArray([2.02034596E9])]|0.0    |
+--------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------+-------+

 

 以下示例也使用相同的概念。 
 

 https://databricks.com/blog/2017/05/09/detecting-abuse-scale-locality-sensitive-hashing-uber-engineering.html 
 

 我想我错过了这个项目的一些基本方面。 请恢复。 
 

 

 它的工作基于user8371915给出的建议。 
 

 我删除了ngram并增加了numHashTables 
 

MinHashLSH minHashLSH = new MinHashLSH().setInputCol("features").setOutputCol("hashValues").setNumHashTables(20);

 

 现在，我能够了解这种匹配的工作原理 
 

 以下是我的两个数据集 
 

 数据集A. 
 

+---+-------------+
|id |text         |
+---+-------------+
|1  |Love is blind|
+---+-------------+

 

 数据集B. 
 

+---+-------------------------+
|id |text                     |
+---+-------------------------+
|1  |Love is blind            |
|2  |Luv is blind             |
|3  |Lov is blind             |
|4  |This is totally different|
|5  |God is love              |
|6  |blind love is divine     |
+---+-------------------------+

 

 最后的输出如下 
 

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
|datasetA                                                                                                                                                                                                                                                                                                                                                                                                                                                             |datasetB                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |distCol|
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]                            |0.0    |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[2,Luv is blind,WrappedArray(luv, is, blind),(262144,[15889,48831,84987],[1.0,1.0,1.0]),WrappedArray([-2.021501434E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-6.70773282E8], [-6.93210471E8], [-1.205754635E9], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [4.46435174E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.036250081E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]                              |0.5    |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[5,God is love,WrappedArray(god, is, love),(262144,[15889,57304,186480],[1.0,1.0,1.0]),WrappedArray([-7.6253133E7], [-2.6669178E7], [-1.590526534E9], [-2.83593282E8], [-1.060055906E9], [-1.411500923E9], [-9.83191394E8], [-8.0411681E7], [-1.04032919E9], [-1.373403353E9], [-5.63413619E8], [-1.240833109E9], [-1.48476096E8], [-1.7390215E9], [-1.745820849E9], [8.1559665E7], [-1.997519365E9], [-1.635066748E9], [6.38995945E8], [-1.59718287E9])]                                        |0.5    |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[6,blind love is divine,WrappedArray(blind, love, is, divine),(262144,[15889,25596,48831,186480],[1.0,1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-1.627956291E9], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.93451596E9], [-1.882820721E9], [-7.50906814E8], [-1.152091375E9], [-1.997519365E9], [-1.380314819E9], [-8.50494401E8], [-1.869738298E9])]|0.25   |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[3,Lov is blind,WrappedArray(lov, is, blind),(262144,[15889,48831,81946],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.88316392E9], [-1.776275893E9], [-6.93210471E8], [-1.39927757E8], [-1.713286948E9], [-1.698342316E9], [-1.164990332E9], [-1.240833109E9], [-1.519529732E9], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.036250081E9], [-1.380314819E9], [-1.808919173E9], [-1.869738298E9])]                            |0.5    |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+


I am trying the below example in java 
 

Efficient string matching in Apache Spark
 

This is my code
 

public class App {
    public static void main(String[] args) {
        System.out.println("Hello World!");

        System.setProperty("hadoop.home.dir", "D:\\del");

        List<MyRecord> firstRow = new ArrayList<MyRecord>();
        firstRow.add(new App().new MyRecord("1", "Love is blind"));

        List<MyRecord> secondRow = new ArrayList<MyRecord>();
        secondRow.add(new App().new MyRecord("1", "Luv is blind"));

        SparkSession spark = SparkSession.builder().appName("LSHExample").config("spark.master", "local")
                .getOrCreate();

        Dataset firstDataFrame = spark.createDataFrame(firstRow, MyRecord.class);
        Dataset secondDataFrame = spark.createDataFrame(secondRow, MyRecord.class);

        firstDataFrame.show(20, false);
        secondDataFrame.show(20, false);

        RegexTokenizer regexTokenizer = new RegexTokenizer().setInputCol("text").setOutputCol("words")
                .setPattern("\\W");
        NGram ngramTransformer = new NGram().setN(3).setInputCol("words").setOutputCol("ngrams");
        HashingTF hashingTF = new HashingTF().setInputCol("ngrams").setOutputCol("vectors");
        MinHashLSH minHashLSH = new MinHashLSH().setInputCol("vectors").setOutputCol("lsh");

        Pipeline pipeline = new Pipeline()
        .setStages(new PipelineStage[] { regexTokenizer, ngramTransformer, hashingTF, minHashLSH });

        PipelineModel model = pipeline.fit(firstDataFrame);

        Dataset dataset1 = model.transform(firstDataFrame);
        dataset1.show(20,false);

        Dataset dataset2 = model.transform(secondDataFrame);
        dataset2 .show(20,false);

        Transformer[] transformers = model.stages();
        MinHashLSHModel temp = (MinHashLSHModel) transformers[transformers.length - 1];
        temp.approxSimilarityJoin(dataset1, dataset2, 0.01).show(20,false);

    }

    protected class MyRecord {
        private String id;
        private String text;

        private MyRecord(String id, String text) {
            this.id = id;
            this.text = text;
        }

        public String getId() {
            return id;
        }

        public String getText() {
            return text;
        }

    }

}

 

Before invoking the approxSimilarityJoin the two datasets look like below.
 

Transformed Dataset A
 

+---+-------------+-----------------+---------------+-----------------------+----------------+
|id |text         |words            |ngrams         |vectors                |lsh             |
+---+-------------+-----------------+---------------+-----------------------+----------------+
|1  |Love is blind|[love, is, blind]|[love is blind]|(262144,[243005],[1.0])|[[2.02034596E9]]|
+---+-------------+-----------------+---------------+-----------------------+----------------+

 

Transformed Dataset B
 

+---+------------+----------------+--------------+----------------------+----------------+
|id |text        |words           |ngrams        |vectors               |lsh             |
+---+------------+----------------+--------------+----------------------+----------------+
|2  |Luv is blind|[luv, is, blind]|[luv is blind]|(262144,[57733],[1.0])|[[7.79808048E8]]|
+---+------------+----------------+--------------+----------------------+----------------+

 

Though the two texts "Love is blind" and "Luv is blind" are almost similar , I get the below blank output.
 

+--------+--------+-------+
|datasetA|datasetB|distCol|
+--------+--------+-------+
+--------+--------+-------+

 

Kindly revert If there is any mistake in the above code .
 

I tested by giving the same input for both data sets and below is the output. The distCol is zero when both the datasets have same text.
 

+--------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------+-------+
|datasetA                                                                                                                        |datasetB                                                                                                                        |distCol|
+--------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------+-------+
|[1,Love is blind,WrappedArray(love, is, blind),WrappedArray(love is blind),(262144,[243005],[1.0]),WrappedArray([2.02034596E9])]|[2,Love is blind,WrappedArray(love, is, blind),WrappedArray(love is blind),(262144,[243005],[1.0]),WrappedArray([2.02034596E9])]|0.0    |
+--------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------+-------+

 

The below example also uses the same concept.
 

https://databricks.com/blog/2017/05/09/detecting-abuse-scale-locality-sensitive-hashing-uber-engineering.html
 

I think I am missing some fundamental aspect in this program. Kindly revert.
 

 

It worked based upon the suggestions given by user8371915 .
 

I removed the ngram and increased the numHashTables
 

MinHashLSH minHashLSH = new MinHashLSH().setInputCol("features").setOutputCol("hashValues").setNumHashTables(20);

 

Now I am able to relate how this matching works
 

Below are my two dataset
 

Dataset A
 

+---+-------------+
|id |text         |
+---+-------------+
|1  |Love is blind|
+---+-------------+

 

Dataset B
 

+---+-------------------------+
|id |text                     |
+---+-------------------------+
|1  |Love is blind            |
|2  |Luv is blind             |
|3  |Lov is blind             |
|4  |This is totally different|
|5  |God is love              |
|6  |blind love is divine     |
+---+-------------------------+

 

and the final output is below
 

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
|datasetA                                                                                                                                                                                                                                                                                                                                                                                                                                                             |datasetB                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |distCol|
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]                            |0.0    |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[2,Luv is blind,WrappedArray(luv, is, blind),(262144,[15889,48831,84987],[1.0,1.0,1.0]),WrappedArray([-2.021501434E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-6.70773282E8], [-6.93210471E8], [-1.205754635E9], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [4.46435174E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.036250081E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]                              |0.5    |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[5,God is love,WrappedArray(god, is, love),(262144,[15889,57304,186480],[1.0,1.0,1.0]),WrappedArray([-7.6253133E7], [-2.6669178E7], [-1.590526534E9], [-2.83593282E8], [-1.060055906E9], [-1.411500923E9], [-9.83191394E8], [-8.0411681E7], [-1.04032919E9], [-1.373403353E9], [-5.63413619E8], [-1.240833109E9], [-1.48476096E8], [-1.7390215E9], [-1.745820849E9], [8.1559665E7], [-1.997519365E9], [-1.635066748E9], [6.38995945E8], [-1.59718287E9])]                                        |0.5    |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[6,blind love is divine,WrappedArray(blind, love, is, divine),(262144,[15889,25596,48831,186480],[1.0,1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-1.627956291E9], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.93451596E9], [-1.882820721E9], [-7.50906814E8], [-1.152091375E9], [-1.997519365E9], [-1.380314819E9], [-8.50494401E8], [-1.869738298E9])]|0.25   |
|[1,Love is blind,WrappedArray(love, is, blind),(262144,[15889,48831,186480],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.815588486E9], [-1.411500923E9], [-6.93210471E8], [-8.0411681E7], [-1.713286948E9], [-1.698342316E9], [-9.33829921E8], [-1.240833109E9], [-1.48476096E8], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.997519365E9], [-1.380314819E9], [-5.92484283E8], [-1.869738298E9])]|[3,Lov is blind,WrappedArray(lov, is, blind),(262144,[15889,48831,81946],[1.0,1.0,1.0]),WrappedArray([-1.06555007E9], [-1.557513224E9], [-1.590526534E9], [-2.83593282E8], [-1.88316392E9], [-1.776275893E9], [-6.93210471E8], [-1.39927757E8], [-1.713286948E9], [-1.698342316E9], [-1.164990332E9], [-1.240833109E9], [-1.519529732E9], [-1.882820721E9], [-7.50906814E8], [1.99715481E8], [-1.036250081E9], [-1.380314819E9], [-1.808919173E9], [-1.869738298E9])]                            |0.5    |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+


原文：https://stackoverflow.com/questions/47618661
更新时间：2023-07-17 07:07

最满意答案

 检查之前输入的index ，如果它少于触发before函数，否则触发after。 然后将当前索引重置为now索引。 
 

 

 
 
 
  
var currentIndex = $("input[name='radio']:checked").index('input');
$("input[name='radio']").change(function() {
  var index = $(this).index('input');
  if (index < currentIndex) {
    console.log('before');
  } else {
    console.log('after');
  }
  currentIndex = index;
});
 
  
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>

<input type="radio" id="a" name="radio">
<label for="a">A</label>

<input type="radio" id="b" name="radio" checked>
<label for="b">B</label>

<input type="radio" id="c" name="radio">
<label for="c">C</label>

<input type="radio" id="d" name="radio">
<label for="d">D</label>
 
 
 

 


Check the index of the input before, if its less then fire the before function, else fire the after. then reset the current index to the now index.
 

 

 
 
 
  
var currentIndex = $("input[name='radio']:checked").index('input');
$("input[name='radio']").change(function() {
  var index = $(this).index('input');
  if (index < currentIndex) {
    console.log('before');
  } else {
    console.log('after');
  }
  currentIndex = index;
});
 
  
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>

<input type="radio" id="a" name="radio">
<label for="a">A</label>

<input type="radio" id="b" name="radio" checked>
<label for="b">B</label>

<input type="radio" id="c" name="radio">
<label for="c">C</label>

<input type="radio" id="d" name="radio">
<label for="d">D</label>

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