/*
* 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 java.util.regex.Pattern;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
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 using MLlib KMeans from Java.
*/
public final class JavaKMeans {
private static class ParsePoint implements Function<String, Vector> {
private static final Pattern SPACE = Pattern.compile(" ");
@Override
public Vector call(String line) {
String[] tok = SPACE.split(line);
double[] point = new double[tok.length];
for (int i = 0; i < tok.length; ++i) {
point[i] = Double.parseDouble(tok[i]);
}
return Vectors.dense(point);
}
}
public static void main(String[] args) {
if (args.length < 3) {
System.err.println(
"Usage: JavaKMeans <input_file> <k> <max_iterations> [<runs>]");
System.exit(1);
}
String inputFile = args[0];
int k = Integer.parseInt(args[1]);
int iterations = Integer.parseInt(args[2]);
int runs = 1;
if (args.length >= 4) {
runs = Integer.parseInt(args[3]);
}
SparkConf sparkConf = new SparkConf().setAppName("JavaKMeans");
JavaSparkContext sc = new JavaSparkContext(sparkConf);
JavaRDD<String> lines = sc.textFile(inputFile);
JavaRDD<Vector> points = lines.map(new ParsePoint());
KMeansModel model = KMeans.train(points.rdd(), k, iterations, runs, KMeans.K_MEANS_PARALLEL());
System.out.println("Cluster centers:");
for (Vector center : model.clusterCenters()) {
System.out.println(" " + center);
}
double cost = model.computeCost(points.rdd());
System.out.println("Cost: " + cost);
sc.stop();
}
}