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.JavaDoubleRDD;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.mllib.linalg.Matrix;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  import org.apache.spark.mllib.stat.Statistics;
  // $example off$
  
  public class JavaCorrelationsExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaCorrelationsExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      JavaDoubleRDD seriesX = jsc.parallelizeDoubles(
        Arrays.asList(1.0, 2.0, 3.0, 3.0, 5.0));  // a series
  
      // must have the same number of partitions and cardinality as seriesX
      JavaDoubleRDD seriesY = jsc.parallelizeDoubles(
        Arrays.asList(11.0, 22.0, 33.0, 33.0, 555.0));
  
      // compute the correlation using Pearson's method. Enter "spearman" for Spearman's method.
      // If a method is not specified, Pearson's method will be used by default.
      Double correlation = Statistics.corr(seriesX.srdd(), seriesY.srdd(), "pearson");
      System.out.println("Correlation is: " + correlation);
  
      // note that each Vector is a row and not a column
      JavaRDD<Vector> data = jsc.parallelize(
        Arrays.asList(
          Vectors.dense(1.0, 10.0, 100.0),
          Vectors.dense(2.0, 20.0, 200.0),
          Vectors.dense(5.0, 33.0, 366.0)
        )
      );
  
      // calculate the correlation matrix using Pearson's method.
      // Use "spearman" for Spearman's method.
      // If a method is not specified, Pearson's method will be used by default.
      Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson");
      System.out.println(correlMatrix.toString());
      // $example off$
  
      jsc.stop();
    }
  }
  
  