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/*
* 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;
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.mllib.clustering.KMeans;
import org.apache.spark.mllib.clustering.KMeansModel;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
// $example off$
public class JavaKMeansExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("JavaKMeansExample");
JavaSparkContext jsc = new JavaSparkContext(conf);
// $example on$
// Load and parse data
String path = "data/mllib/kmeans_data.txt";
JavaRDD<String> data = jsc.textFile(path);
JavaRDD<Vector> parsedData = data.map(s -> {
String[] sarray = s.split(" ");
double[] values = new double[sarray.length];
for (int i = 0; i < sarray.length; i++) {
values[i] = Double.parseDouble(sarray[i]);
}
return Vectors.dense(values);
});
parsedData.cache();
// Cluster the data into two classes using KMeans
int numClusters = 2;
int numIterations = 20;
KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations);
System.out.println("Cluster centers:");
for (Vector center: clusters.clusterCenters()) {
System.out.println(" " + center);
}
double cost = clusters.computeCost(parsedData.rdd());
System.out.println("Cost: " + cost);
// Evaluate clustering by computing Within Set Sum of Squared Errors
double WSSSE = clusters.computeCost(parsedData.rdd());
System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
// Save and load model
clusters.save(jsc.sc(), "target/org/apache/spark/JavaKMeansExample/KMeansModel");
KMeansModel sameModel = KMeansModel.load(jsc.sc(),
"target/org/apache/spark/JavaKMeansExample/KMeansModel");
// $example off$
jsc.stop();
}
}
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