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   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * 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
   * the License.  You may obtain a copy of the License at
   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package org.apache.spark.ml.feature;
  
  import java.util.Arrays;
  
  import static org.apache.spark.sql.types.DataTypes.*;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.linalg.Vector;
  import org.apache.spark.ml.linalg.VectorUDT;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.types.StructField;
  import org.apache.spark.sql.types.StructType;
  
  public class JavaVectorAssemblerSuite extends SharedSparkSession {
  
    @Test
    public void testVectorAssembler() {
      StructType schema = createStructType(new StructField[]{
        createStructField("id", IntegerType, false),
        createStructField("x", DoubleType, false),
        createStructField("y", new VectorUDT(), false),
        createStructField("name", StringType, false),
        createStructField("z", new VectorUDT(), false),
        createStructField("n", LongType, false)
      });
      Row row = RowFactory.create(
        0, 0.0, Vectors.dense(1.0, 2.0), "a",
        Vectors.sparse(2, new int[]{1}, new double[]{3.0}), 10L);
      Dataset<Row> dataset = spark.createDataFrame(Arrays.asList(row), schema);
      VectorAssembler assembler = new VectorAssembler()
        .setInputCols(new String[]{"x", "y", "z", "n"})
        .setOutputCol("features");
      Dataset<Row> output = assembler.transform(dataset);
      Assert.assertEquals(
        Vectors.sparse(6, new int[]{1, 2, 4, 5}, new double[]{1.0, 2.0, 3.0, 10.0}),
        output.select("features").first().<Vector>getAs(0));
    }
  }
  