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.util.Arrays;
  import java.util.List;
  
  import static org.junit.Assert.assertEquals;
  
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  
  public class JavaGaussianMixtureSuite extends SharedSparkSession {
  
    @Test
    public void runGaussianMixture() {
      List<Vector> points = Arrays.asList(
        Vectors.dense(1.0, 2.0, 6.0),
        Vectors.dense(1.0, 3.0, 0.0),
        Vectors.dense(1.0, 4.0, 6.0)
      );
  
      JavaRDD<Vector> data = jsc.parallelize(points, 2);
      GaussianMixtureModel model = new GaussianMixture().setK(2).setMaxIterations(1).setSeed(1234)
        .run(data);
      assertEquals(model.gaussians().length, 2);
      JavaRDD<Integer> predictions = model.predict(data);
      predictions.first();
    }
  }
  