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 java.util.Arrays;
  
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.mllib.stat.KernelDensity;
  // $example off$
  
  public class JavaKernelDensityEstimationExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaKernelDensityEstimationExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      // an RDD of sample data
      JavaRDD<Double> data = jsc.parallelize(
        Arrays.asList(1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 6.0, 7.0, 8.0, 9.0, 9.0));
  
      // Construct the density estimator with the sample data
      // and a standard deviation for the Gaussian kernels
      KernelDensity kd = new KernelDensity().setSample(data).setBandwidth(3.0);
  
      // Find density estimates for the given values
      double[] densities = kd.estimate(new double[]{-1.0, 2.0, 5.0});
  
      System.out.println(Arrays.toString(densities));
      // $example off$
  
      jsc.stop();
    }
  }
  
  