| /* |
| * 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.flink.test.iterative; |
| |
| import org.apache.flink.api.common.functions.MapFunction; |
| import org.apache.flink.api.java.DataSet; |
| import org.apache.flink.api.java.ExecutionEnvironment; |
| import org.apache.flink.api.java.operators.IterativeDataSet; |
| import org.apache.flink.examples.java.clustering.KMeans; |
| import org.apache.flink.examples.java.clustering.KMeans.Centroid; |
| import org.apache.flink.examples.java.clustering.KMeans.Point; |
| import org.apache.flink.test.testdata.KMeansData; |
| import org.apache.flink.test.util.JavaProgramTestBase; |
| |
| import java.util.List; |
| import java.util.Locale; |
| |
| /** Test KMeans clustering with a broadcast set. */ |
| public class KMeansWithBroadcastSetITCase extends JavaProgramTestBase { |
| |
| @SuppressWarnings("serial") |
| @Override |
| protected void testProgram() throws Exception { |
| |
| String[] points = KMeansData.DATAPOINTS_2D.split("\n"); |
| String[] centers = KMeansData.INITIAL_CENTERS_2D.split("\n"); |
| |
| // set up execution environment |
| ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); |
| |
| // get input data |
| DataSet<Point> pointsSet = |
| env.fromElements(points) |
| .map( |
| new MapFunction<String, Point>() { |
| public Point map(String p) { |
| String[] fields = p.split("\\|"); |
| return new Point( |
| Double.parseDouble(fields[1]), |
| Double.parseDouble(fields[2])); |
| } |
| }); |
| |
| DataSet<Centroid> centroidsSet = |
| env.fromElements(centers) |
| .map( |
| new MapFunction<String, Centroid>() { |
| public Centroid map(String c) { |
| String[] fields = c.split("\\|"); |
| return new Centroid( |
| Integer.parseInt(fields[0]), |
| Double.parseDouble(fields[1]), |
| Double.parseDouble(fields[2])); |
| } |
| }); |
| |
| // set number of bulk iterations for KMeans algorithm |
| IterativeDataSet<Centroid> loop = centroidsSet.iterate(20); |
| |
| DataSet<Centroid> newCentroids = |
| pointsSet |
| // compute closest centroid for each point |
| .map(new KMeans.SelectNearestCenter()) |
| .withBroadcastSet(loop, "centroids") |
| // count and sum point coordinates for each centroid |
| .map(new KMeans.CountAppender()) |
| .groupBy(0) |
| .reduce(new KMeans.CentroidAccumulator()) |
| // compute new centroids from point counts and coordinate sums |
| .map(new KMeans.CentroidAverager()); |
| |
| // feed new centroids back into next iteration |
| DataSet<Centroid> finalCentroids = loop.closeWith(newCentroids); |
| |
| DataSet<String> stringCentroids = |
| finalCentroids.map( |
| new MapFunction<Centroid, String>() { |
| @Override |
| public String map(Centroid c) throws Exception { |
| return String.format(Locale.US, "%d|%.2f|%.2f|", c.id, c.x, c.y); |
| } |
| }); |
| |
| List<String> result = stringCentroids.collect(); |
| |
| KMeansData.checkResultsWithDelta( |
| KMeansData.CENTERS_2D_AFTER_20_ITERATIONS_DOUBLE_DIGIT, result, 0.01); |
| } |
| } |