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.mllib;
  
  import org.apache.spark.SparkConf;
  import org.apache.spark.api.java.JavaSparkContext;
  
  // $example on$
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.mllib.clustering.GaussianMixture;
  import org.apache.spark.mllib.clustering.GaussianMixtureModel;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  // $example off$
  
  public class JavaGaussianMixtureExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaGaussianMixtureExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      // Load and parse data
      String path = "data/mllib/gmm_data.txt";
      JavaRDD<String> data = jsc.textFile(path);
      JavaRDD<Vector> parsedData = data.map(
        new Function<String, Vector>() {
          public Vector call(String s) {
            String[] sarray = s.trim().split(" ");
            double[] values = new double[sarray.length];
            for (int i = 0; i < sarray.length; i++) {
              values[i] = Double.parseDouble(sarray[i]);
            }
            return Vectors.dense(values);
          }
        }
      );
      parsedData.cache();
  
      // Cluster the data into two classes using GaussianMixture
      GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd());
  
      // Save and load GaussianMixtureModel
      gmm.save(jsc.sc(), "target/org/apache/spark/JavaGaussianMixtureExample/GaussianMixtureModel");
      GaussianMixtureModel sameModel = GaussianMixtureModel.load(jsc.sc(),
        "target/org.apache.spark.JavaGaussianMixtureExample/GaussianMixtureModel");
  
      // Output the parameters of the mixture model
      for (int j = 0; j < gmm.k(); j++) {
        System.out.printf("weight=%f\nmu=%s\nsigma=\n%s\n",
          gmm.weights()[j], gmm.gaussians()[j].mu(), gmm.gaussians()[j].sigma());
      }
      // $example off$
  
      jsc.stop();
    }
  }
  