public void covariance()
{
covMatrix = new double[2][];
for (int i = 0; i < 2; i++)
covMatrix[i] = new double[2];
V = new double[2][];
for (int i = 0; i < 2; i++)
V[i] = new double[2];
d = new double[2];
// //covMatrix[0][0] = 0;
// //covMatrix[0][1] = 0;
// //covMatrix[1][0] = 0;
// //covMatrix[1][1] = 0;
for (int i = 0; i < totalCount; i++)
{
covMatrix[0][0] += (intermediatePoints[i].X - meanX) * (intermediatePoints[i].X - meanX);
covMatrix[0][1] += (intermediatePoints[i].X - meanX) * (intermediatePoints[i].Y - meanY);
covMatrix[1][0] = covMatrix[0][1];
covMatrix[1][1] += (intermediatePoints[i].Y - meanY) * (intermediatePoints[i].Y - meanY);
}
covMatrix[0][0] = covMatrix[0][0] / (totalCount - 1);
covMatrix[0][1] = covMatrix[0][1] / (totalCount - 1);
covMatrix[1][0] = covMatrix[1][0] / (totalCount - 1);
covMatrix[1][1] = covMatrix[1][1] / (totalCount - 1);
// Console.WriteLine(" testing covariance matrix: {0} {1} {2} {3}", covMatrix[0][0], covMatrix[0][1], covMatrix[1][0], covMatrix[1][1]);
// Console.WriteLine(" matrix testing : {0}", covMatrix);
//yeta baata eigenvalue ra eigenvector niskincha
GeneralMatrix generalMatrix = new GeneralMatrix(covMatrix);
EigenvalueDecomposition eigenvalueDecomposition = new EigenvalueDecomposition(generalMatrix);
d = eigenvalueDecomposition.getD(); // get eigen values
V = eigenvalueDecomposition.getV(); // get eigen vectors
//for (int i = 0; i < 2; i++)
//Console.WriteLine("eigenvalue: " + d[i]);
//for (int i = 0; i < 2; i++)
//{
// for (int j = 0; j < 2; j++)
// Console.WriteLine("eigenvector " + V[i][j]);
//}
}
