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;
  
  // $example on$
  import java.util.Arrays;
  import java.util.List;
  
  import org.apache.spark.ml.feature.Word2Vec;
  import org.apache.spark.ml.feature.Word2VecModel;
  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.SparkSession;
  import org.apache.spark.sql.types.*;
  // $example off$
  
  public class JavaWord2VecExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaWord2VecExample")
        .getOrCreate();
  
      // $example on$
      // Input data: Each row is a bag of words from a sentence or document.
      List<Row> data = 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(" ")))
      );
      StructType schema = new StructType(new StructField[]{
        new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
      });
      Dataset<Row> documentDF = spark.createDataFrame(data, schema);
  
      // Learn a mapping from words to Vectors.
      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 row : result.collectAsList()) {
        List<String> text = row.getList(0);
        Vector vector = (Vector) row.get(1);
        System.out.println("Text: " + text + " => \nVector: " + vector + "\n");
      }
      // $example off$
  
      spark.stop();
    }
  }
  