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.mllib;
  
  // $example on$
  import java.util.Arrays;
  // $example off$
  
  import org.apache.spark.SparkConf;
  import org.apache.spark.api.java.JavaSparkContext;
  // $example on$
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.mllib.feature.ElementwiseProduct;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  // $example off$
  import org.apache.spark.api.java.function.VoidFunction;
  
  public class JavaElementwiseProductExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaElementwiseProductExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      // Create some vector data; also works for sparse vectors
      JavaRDD<Vector> data = jsc.parallelize(Arrays.asList(
        Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)));
      Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0);
      final ElementwiseProduct transformer = new ElementwiseProduct(transformingVector);
  
      // Batch transform and per-row transform give the same results:
      JavaRDD<Vector> transformedData = transformer.transform(data);
      JavaRDD<Vector> transformedData2 = data.map(
        new Function<Vector, Vector>() {
          @Override
          public Vector call(Vector v) {
            return transformer.transform(v);
          }
        }
      );
      // $example off$
  
      System.out.println("transformedData: ");
      transformedData.foreach(new VoidFunction<Vector>() {
        @Override
        public void call(Vector vector) throws Exception {
          System.out.println(vector.toString());
        }
      });
  
      System.out.println("transformedData2: ");
      transformedData2.foreach(new VoidFunction<Vector>() {
        @Override
        public void call(Vector vector) throws Exception {
          System.out.println(vector.toString());
        }
      });
  
      jsc.stop();
    }
  }
  