spark-gpu-master/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaBisectingKMeansSuite.java
/*
 * 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 com.google.common.collect.Lists;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;

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 JavaBisectingKMeansSuite implements Serializable {
  private transient JavaSparkContext sc;

  @Before
  public void setUp() {
    sc = new JavaSparkContext("local", this.getClass().getSimpleName());
  }

  @After
  public void tearDown() {
    sc.stop();
    sc = null;
  }

  @Test
  public void twoDimensionalData() {
    JavaRDD<Vector> points = sc.parallelize(Lists.newArrayList(
      Vectors.dense(4, -1),
      Vectors.dense(4, 1),
      Vectors.sparse(2, new int[] {0}, new double[] {1.0})
    ), 2);

    BisectingKMeans bkm = new BisectingKMeans()
      .setK(4)
      .setMaxIterations(2)
      .setSeed(1L);
    BisectingKMeansModel model = bkm.run(points);
    Assert.assertEquals(3, model.k());
    Assert.assertArrayEquals(new double[] {3.0, 0.0}, model.root().center().toArray(), 1e-12);
    for (ClusteringTreeNode child: model.root().children()) {
      double[] center = child.center().toArray();
      if (center[0] > 2) {
        Assert.assertEquals(2, child.size());
        Assert.assertArrayEquals(new double[] {4.0, 0.0}, center, 1e-12);
      } else {
        Assert.assertEquals(1, child.size());
        Assert.assertArrayEquals(new double[] {1.0, 0.0}, center, 1e-12);
      }
    }
  }
}
