examples/mllib/JavaElementwiseProductExample.java

0001 /*
0002  * Licensed to the Apache Software Foundation (ASF) under one or more
0003  * contributor license agreements.  See the NOTICE file distributed with
0004  * this work for additional information regarding copyright ownership.
0005  * The ASF licenses this file to You under the Apache License, Version 2.0
0006  * (the "License"); you may not use this file except in compliance with
0007  * the License.  You may obtain a copy of the License at
0008  *
0009  *    http://www.apache.org/licenses/LICENSE-2.0
0010  *
0011  * Unless required by applicable law or agreed to in writing, software
0012  * distributed under the License is distributed on an "AS IS" BASIS,
0013  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014  * See the License for the specific language governing permissions and
0015  * limitations under the License.
0016  */
0017
0018 package org.apache.spark.examples.mllib;
0019
0020 // $example on$
0021 import java.util.Arrays;
0022 // $example off$
0023
0024 import org.apache.spark.SparkConf;
0025 import org.apache.spark.api.java.JavaSparkContext;
0026 // $example on$
0027 import org.apache.spark.api.java.JavaRDD;
0028 import org.apache.spark.mllib.feature.ElementwiseProduct;
0029 import org.apache.spark.mllib.linalg.Vector;
0030 import org.apache.spark.mllib.linalg.Vectors;
0031 // $example off$
0032
0033 public class JavaElementwiseProductExample {
0034   public static void main(String[] args) {
0035
0036     SparkConf conf = new SparkConf().setAppName("JavaElementwiseProductExample");
0037     JavaSparkContext jsc = new JavaSparkContext(conf);
0038
0039     // $example on$
0040     // Create some vector data; also works for sparse vectors
0041     JavaRDD<Vector> data = jsc.parallelize(Arrays.asList(
0042       Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)));
0043     Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0);
0044     ElementwiseProduct transformer = new ElementwiseProduct(transformingVector);
0045
0046     // Batch transform and per-row transform give the same results:
0047     JavaRDD<Vector> transformedData = transformer.transform(data);
0048     JavaRDD<Vector> transformedData2 = data.map(transformer::transform);
0049     // $example off$
0050
0051     System.out.println("transformedData: ");
0052     transformedData.foreach(System.out::println);
0053
0054     System.out.println("transformedData2: ");
0055     transformedData2.foreach(System.out::println);
0056
0057     jsc.stop();
0058   }
0059 }