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   * Licensed to the Apache Software Foundation (ASF) under one or more
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   * this work for additional information regarding copyright ownership.
   * 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
   *
   * Unless required by applicable law or agreed to in writing, software
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  package org.apache.spark.ml.feature;
  
  import java.util.Arrays;
  import java.util.List;
  import java.util.Map;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.feature.VectorIndexerSuite.FeatureData;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  
  
  public class JavaVectorIndexerSuite extends SharedSparkSession {
  
    @Test
    public void vectorIndexerAPI() {
      // The tests are to check Java compatibility.
      List<FeatureData> points = Arrays.asList(
        new FeatureData(Vectors.dense(0.0, -2.0)),
        new FeatureData(Vectors.dense(1.0, 3.0)),
        new FeatureData(Vectors.dense(1.0, 4.0))
      );
      Dataset<Row> data = spark.createDataFrame(jsc.parallelize(points, 2), FeatureData.class);
      VectorIndexer indexer = new VectorIndexer()
        .setInputCol("features")
        .setOutputCol("indexed")
        .setMaxCategories(2);
      VectorIndexerModel model = indexer.fit(data);
      Assert.assertEquals(model.numFeatures(), 2);
      Map<Integer, Map<Double, Integer>> categoryMaps = model.javaCategoryMaps();
      Assert.assertEquals(categoryMaps.size(), 1);
      Dataset<Row> indexedData = model.transform(data);
    }
  }
  