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.examples.ml;
  
  // $example on$
  import org.apache.spark.ml.clustering.GaussianMixture;
  import org.apache.spark.ml.clustering.GaussianMixtureModel;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  // $example off$
  import org.apache.spark.sql.SparkSession;
  
  
  /**
   * An example demonstrating Gaussian Mixture Model.
   * Run with
   * <pre>
   * bin/run-example ml.JavaGaussianMixtureExample
   * </pre>
   */
  public class JavaGaussianMixtureExample {
  
    public static void main(String[] args) {
  
      // Creates a SparkSession
      SparkSession spark = SparkSession
              .builder()
              .appName("JavaGaussianMixtureExample")
              .getOrCreate();
  
      // $example on$
      // Loads data
      Dataset<Row> dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt");
  
      // Trains a GaussianMixture model
      GaussianMixture gmm = new GaussianMixture()
        .setK(2);
      GaussianMixtureModel model = gmm.fit(dataset);
  
      // Output the parameters of the mixture model
      for (int i = 0; i < model.getK(); i++) {
        System.out.printf("Gaussian %d:\nweight=%f\nmu=%s\nsigma=\n%s\n\n",
                i, model.weights()[i], model.gaussians()[i].mean(), model.gaussians()[i].cov());
      }
      // $example off$
  
      spark.stop();
    }
  }
  