content: /*
   * 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.examples.ml;
  
  import org.apache.spark.sql.SparkSession;
  
  // $example on$
  import java.util.Arrays;
  
  import org.apache.spark.ml.feature.VectorAssembler;
  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.*;
  import static org.apache.spark.sql.types.DataTypes.*;
  // $example off$
  
  public class JavaVectorAssemblerExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaVectorAssemblerExample")
        .getOrCreate();
  
      // $example on$
      StructType schema = createStructType(new StructField[]{
        createStructField("id", IntegerType, false),
        createStructField("hour", IntegerType, false),
        createStructField("mobile", DoubleType, false),
        createStructField("userFeatures", new VectorUDT(), false),
        createStructField("clicked", DoubleType, false)
      });
      Row row = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0);
      Dataset<Row> dataset = spark.createDataFrame(Arrays.asList(row), schema);
  
      VectorAssembler assembler = new VectorAssembler()
        .setInputCols(new String[]{"hour", "mobile", "userFeatures"})
        .setOutputCol("features");
  
      Dataset<Row> output = assembler.transform(dataset);
      System.out.println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column " +
          "'features'");
      output.select("features", "clicked").show(false);
      // $example off$
  
      spark.stop();
    }
  }
  
  