Day 7: Pearson Correlation Coefficient I HackerRank Solution

Day 7: Pearson Correlation Coefficient I HackerRank Solution
Source : https://www.hackerrank.com/challenges/s10-pearson-correlation-coefficient

Objective
In this challenge, we practice calculating the Pearson correlation coefficient. Check out the Tutorial tab for learning materials!

Task
Given two -element data sets, and , calculate the value of the Pearson correlation coefficient.

Input Format

The first line contains an integer, , denoting the size of data sets and .
The second line contains space-separated real numbers (scaled to at most one decimal place), defining data set .
The third line contains space-separated real numbers (scaled to at most one decimal place), defining data set .

Constraints

• , where is the value of data set .
• , where is the value of data set .
• Data set contains unique values.
• Data set contains unique values.

Output Format

Print the value of the Pearson correlation coefficient, rounded to a scale of decimal places.

Sample Input

10
10 9.8 8 7.8 7.7 7 6 5 4 2 
200 44 32 24 22 17 15 12 8 4

Sample Output

0.612

Explanation

The mean and standard deviation of data set are:

The mean and standard deviation of data set are:

We use the following formula to calculate the Pearson correlation coefficient:

Source : https://www.hackerrank.com/challenges/s10-pearson-correlation-coefficient

Solution

	// Karthikalapati.blogspot.com
	import java.util.Scanner;
	public class Solution {
	public static void main(String[] args) {
	/* Save input */
	Scanner scan = new Scanner(System.in);
	int size = scan.nextInt();
	double [] xs = new double[size];
	double [] ys = new double[size];
	for (int i = 0; i < size; i++) {
	xs[i] = scan.nextDouble();
	}
	for (int i = 0; i < size; i++) {
	ys[i] = scan.nextDouble();
	}
	scan.close();
	System.out.println(pearson(xs, ys));
	}
	/* Calculates Pearson coefficient */
	private static Double pearson(double [] xs, double [] ys) {
	if (xs == null || ys == null || xs.length != ys.length) {
	return null;
	}
	double xMean = getMean(xs);
	double yMean = getMean(xs);
	int n = xs.length;
	double numerator = 0;
	for (int i = 0; i < n; i++) {
	numerator += (xs[i] - xMean) * (ys[i] - yMean);
	}
	return numerator / (n * standardDeviation(xs) * standardDeviation(ys));
	}
	private static Double getMean(double [] array) {
	if (array == null) {
	return null;
	}
	double total = 0;
	for (double num : array) {
	total += num;
	}
	return total / array.length;
	}
	private static Double standardDeviation(double [] array) {
	if (array == null) {
	return null;
	}
	double mean = getMean(array);
	double sum = 0;
	for (double x : array) {
	sum += Math.pow(x - mean, 2);
	}
	double variance = sum / array.length;
	return Math.sqrt(variance);
	}
	}