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.Dataset;
  import org.apache.spark.sql.SparkSession;
  
  // $example on$
  import java.util.Arrays;
  import java.util.List;
  
  import org.apache.spark.ml.feature.Binarizer;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.types.DataTypes;
  import org.apache.spark.sql.types.Metadata;
  import org.apache.spark.sql.types.StructField;
  import org.apache.spark.sql.types.StructType;
  // $example off$
  
  public class JavaBinarizerExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaBinarizerExample")
        .getOrCreate();
  
      // $example on$
      List<Row> data = Arrays.asList(
        RowFactory.create(0, 0.1),
        RowFactory.create(1, 0.8),
        RowFactory.create(2, 0.2)
      );
      StructType schema = new StructType(new StructField[]{
        new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
        new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
      });
      Dataset<Row> continuousDataFrame = spark.createDataFrame(data, schema);
  
      Binarizer binarizer = new Binarizer()
        .setInputCol("feature")
        .setOutputCol("binarized_feature")
        .setThreshold(0.5);
  
      Dataset<Row> binarizedDataFrame = binarizer.transform(continuousDataFrame);
  
      System.out.println("Binarizer output with Threshold = " + binarizer.getThreshold());
      binarizedDataFrame.show();
      // $example off$
  
      spark.stop();
    }
  }
  