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.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  import org.apache.spark.mllib.stat.MultivariateStatisticalSummary;
  import org.apache.spark.mllib.stat.Statistics;
  // $example off$
  
  public class JavaSummaryStatisticsExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaSummaryStatisticsExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      JavaRDD<Vector> mat = jsc.parallelize(
        Arrays.asList(
          Vectors.dense(1.0, 10.0, 100.0),
          Vectors.dense(2.0, 20.0, 200.0),
          Vectors.dense(3.0, 30.0, 300.0)
        )
      ); // an RDD of Vectors
  
      // Compute column summary statistics.
      MultivariateStatisticalSummary summary = Statistics.colStats(mat.rdd());
      System.out.println(summary.mean());  // a dense vector containing the mean value for each column
      System.out.println(summary.variance());  // column-wise variance
      System.out.println(summary.numNonzeros());  // number of nonzeros in each column
      // $example off$
  
      jsc.stop();
    }
  }
  