#include <iostream.h>
#include <conio.h>
#include <math.h>

//------------sourcea.ir----------------------------------//
#include <stdio.h>

class StdDeviation
{
private:

	int max;

	double value[100];

	double mean;

public:
	double CalculateMean()
	{
		double sum = 0;

		for (int i = 0; i < max; i++)

			sum += value[i];

		return (sum / max);
	}

	double CalculateVariane()
	{
		mean = CalculateMean();
		double temp = 0;

		for (int i = 0; i < max; i++)
		{
			temp += (value[i] - mean) * (value[i] - mean);
		}
		return temp / max;
	}

	double CalculateSampleVariane()
	{
		mean = CalculateMean();
		double temp = 0;
		for (int i = 0; i < max; i++)
		{
			temp += (value[i] - mean) * (value[i] - mean);
		}
		return temp / (max - 1);
	}
	int SetValues(double *p, int count)
	{
		if (count > 100)

			return -1;

		max = count;

		for (int i = 0; i < count; i++)

			value[i] = p[i];

		return 0;
	}

	double Calculate_StandardDeviation()
	{
		return sqrt(CalculateVariane());
	}

	double Calculate_SampleStandardDeviation()
	{
		return sqrt(CalculateSampleVariane());
	}
};

class FinanceCalculator
{
private:

	double XSeries[100];

	double YSeries[100];

	int max;
	StdDeviation x;

	StdDeviation y;
public:

	void SetValues(double *xvalues, double *yvalues, int count)
	{
		for (int i = 0; i < count; i++)
		{
			XSeries[i] = xvalues[i];

			YSeries[i] = yvalues[i];
		}
		x.SetValues(xvalues, count);
		y.SetValues(yvalues, count);
		max = count;
	}
	double Calculate_Covariance()
	{
		double xmean = x.CalculateMean();

		double ymean = y.CalculateMean();
		double total = 0;
		for (int i = 0; i < max; i++)
		{
			total += (XSeries[i] - xmean) * (YSeries[i] - ymean);
		}
		return total / max;
	}

	double Calculate_Correlation()
	{
		double cov = Calculate_Covariance();
		double correlation = cov / (x.Calculate_StandardDeviation() * y.Calculate_StandardDeviation());
		return correlation;
	}

};

void main()
{
	FinanceCalculator calc;
	{
		printf("\n\nZero Correlation and Covariance Data Set\n");

		double xarr[] = { 8, 6, 4, 6, 8 };

		double yarr[] = { 10, 12, 14, 16, 18 };
		calc.SetValues(xarr, yarr, sizeof(xarr) / sizeof(xarr[0]));
		printf("Covariance = %.10lf\n", calc.Calculate_Covariance());

		printf("Correlation = %.10lf\n", calc.Calculate_Correlation());
	}
	{
		printf("\n\nPositive Correlation and Low Covariance Data Set\n");

		double xarr[] = { 0, 2, 4, 6, 8 };

		double yarr[] = { 6, 13, 15, 16, 20 };
		calc.SetValues(xarr, yarr, sizeof(xarr) / sizeof(xarr[0]));
		printf("Covariance = %.10lf\n", calc.Calculate_Covariance());
		printf("Correlation = %.10lf\n", calc.Calculate_Correlation());
	}
	{
		printf("\n\nNegative Correlation and Low Covariance Data Set\n");
		double xarr[] = { 8, 6, 4, 2, 0 };
		double yarr[] = { 6, 13, 15, 16, 20 };
		calc.SetValues(xarr, yarr, sizeof(xarr) / sizeof(xarr[0]));
		printf("Covariance = %.10lf\n", calc.Calculate_Covariance());
		printf("Correlation = %.10lf\n", calc.Calculate_Correlation());
	}
	{
		printf("\n\nPositive Correlation and High Covariance Data Set\n");
		double xarr[] = { 8, 6, 4, 2, 0 };
		double yarr[] = { 1006, 513, 315, 216, 120 };
		calc.SetValues(xarr, yarr, sizeof(xarr) / sizeof(xarr[0]));
		printf("Covariance = %.10lf\n", calc.Calculate_Covariance());
		printf("Correlation = %.10lf\n", calc.Calculate_Correlation());
	}

	{

		printf("\n\nNegative Correlation and High Covariance Data Set\n");
		double xarr[] = { 8, 6, 4, 2, 0 };
		double yarr[] = { 120, 216, 315, 513, 1006 };
		calc.SetValues(xarr, yarr, sizeof(xarr) / sizeof(xarr[0]));
		printf("Covariance = %.10lf\n", calc.Calculate_Covariance());
		printf("Correlation = %.10lf\n", calc.Calculate_Correlation());
	}
}