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.clustering;
  
  import java.io.Serializable;
  import java.util.List;
  
  import org.junit.After;
  import org.junit.Before;
  import org.junit.Test;
  import static org.junit.Assert.*;
  
  import com.google.common.collect.Lists;
  
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.JavaSparkContext;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  
  public class JavaKMeansSuite implements Serializable {
    private transient JavaSparkContext sc;
  
    @Before
    public void setUp() {
      sc = new JavaSparkContext("local", "JavaKMeans");
    }
  
    @After
    public void tearDown() {
      sc.stop();
      sc = null;
      System.clearProperty("spark.driver.port");
    }
  
    @Test
    public void runKMeansUsingStaticMethods() {
      List<Vector> points = Lists.newArrayList(
        Vectors.dense(1.0, 2.0, 6.0),
        Vectors.dense(1.0, 3.0, 0.0),
        Vectors.dense(1.0, 4.0, 6.0)
      );
  
      Vector expectedCenter = Vectors.dense(1.0, 3.0, 4.0);
  
      JavaRDD<Vector> data = sc.parallelize(points, 2);
      KMeansModel model = KMeans.train(data.rdd(), 1, 1, 1, KMeans.K_MEANS_PARALLEL());
      assertEquals(1, model.clusterCenters().length);
      assertEquals(expectedCenter, model.clusterCenters()[0]);
  
      model = KMeans.train(data.rdd(), 1, 1, 1, KMeans.RANDOM());
      assertEquals(expectedCenter, model.clusterCenters()[0]);
    }
  
    @Test
    public void runKMeansUsingConstructor() {
      List<Vector> points = Lists.newArrayList(
        Vectors.dense(1.0, 2.0, 6.0),
        Vectors.dense(1.0, 3.0, 0.0),
        Vectors.dense(1.0, 4.0, 6.0)
      );
  
      Vector expectedCenter = Vectors.dense(1.0, 3.0, 4.0);
  
      JavaRDD<Vector> data = sc.parallelize(points, 2);
      KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd());
      assertEquals(1, model.clusterCenters().length);
      assertEquals(expectedCenter, model.clusterCenters()[0]);
  
      model = new KMeans()
        .setK(1)
        .setMaxIterations(1)
        .setInitializationMode(KMeans.RANDOM())
        .run(data.rdd());
      assertEquals(expectedCenter, model.clusterCenters()[0]);
    }
  }
  