/*
 * 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();
  }
}

