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   * Licensed to the Apache Software Foundation (ASF) under one or more
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   * The ASF licenses this file to You under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
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   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
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  package org.apache.spark.ml.feature;
  
  import java.util.Arrays;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.linalg.Vector;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.types.*;
  
  public class JavaWord2VecSuite extends SharedSparkSession {
  
    @Test
    public void testJavaWord2Vec() {
      StructType schema = new StructType(new StructField[]{
        new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
      });
      Dataset<Row> documentDF = spark.createDataFrame(
        Arrays.asList(
          RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
          RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
          RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))),
        schema);
  
      Word2Vec word2Vec = new Word2Vec()
        .setInputCol("text")
        .setOutputCol("result")
        .setVectorSize(3)
        .setMinCount(0);
      Word2VecModel model = word2Vec.fit(documentDF);
      Dataset<Row> result = model.transform(documentDF);
  
      for (Row r : result.select("result").collectAsList()) {
        double[] polyFeatures = ((Vector) r.get(0)).toArray();
        Assert.assertEquals(polyFeatures.length, 3);
      }
    }
  }
  