content: /*
   * 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.mllib.examples;
  
  import java.util.regex.Pattern;
  
  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 < 4) {
        System.err.println(
            "Usage: JavaKMeans <master> <input_file> <k> <max_iterations> [<runs>]");
        System.exit(1);
      }
  
      String inputFile = args[1];
      int k = Integer.parseInt(args[2]);
      int iterations = Integer.parseInt(args[3]);
      int runs = 1;
  
      if (args.length >= 5) {
        runs = Integer.parseInt(args[4]);
      }
  
      JavaSparkContext sc = new JavaSparkContext(args[0], "JavaKMeans",
          System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaKMeans.class));
      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);
  
      System.exit(0);
    }
  }
  