examples/mllib/JavaKMeansExample.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 import org.apache.spark.SparkConf;
0021 import org.apache.spark.api.java.JavaSparkContext;
0022
0023 // $example on$
0024 import org.apache.spark.api.java.JavaRDD;
0025 import org.apache.spark.mllib.clustering.KMeans;
0026 import org.apache.spark.mllib.clustering.KMeansModel;
0027 import org.apache.spark.mllib.linalg.Vector;
0028 import org.apache.spark.mllib.linalg.Vectors;
0029 // $example off$
0030
0031 public class JavaKMeansExample {
0032   public static void main(String[] args) {
0033
0034     SparkConf conf = new SparkConf().setAppName("JavaKMeansExample");
0035     JavaSparkContext jsc = new JavaSparkContext(conf);
0036
0037     // $example on$
0038     // Load and parse data
0039     String path = "data/mllib/kmeans_data.txt";
0040     JavaRDD<String> data = jsc.textFile(path);
0041     JavaRDD<Vector> parsedData = data.map(s -> {
0042       String[] sarray = s.split(" ");
0043       double[] values = new double[sarray.length];
0044       for (int i = 0; i < sarray.length; i++) {
0045         values[i] = Double.parseDouble(sarray[i]);
0046       }
0047       return Vectors.dense(values);
0048     });
0049     parsedData.cache();
0050
0051     // Cluster the data into two classes using KMeans
0052     int numClusters = 2;
0053     int numIterations = 20;
0054     KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations);
0055
0056     System.out.println("Cluster centers:");
0057     for (Vector center: clusters.clusterCenters()) {
0058       System.out.println(" " + center);
0059     }
0060     double cost = clusters.computeCost(parsedData.rdd());
0061     System.out.println("Cost: " + cost);
0062
0063     // Evaluate clustering by computing Within Set Sum of Squared Errors
0064     double WSSSE = clusters.computeCost(parsedData.rdd());
0065     System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
0066
0067     // Save and load model
0068     clusters.save(jsc.sc(), "target/org/apache/spark/JavaKMeansExample/KMeansModel");
0069     KMeansModel sameModel = KMeansModel.load(jsc.sc(),
0070       "target/org/apache/spark/JavaKMeansExample/KMeansModel");
0071     // $example off$
0072
0073     jsc.stop();
0074   }
0075 }