void bug987()
{
Matrix3Xd points = Matrix3Xd::Random(3, 3);
Vector2d diag = Vector2d::Random();
Matrix2Xd tmp1 = points.topRows<2>(), res1, res2;
VERIFY_IS_APPROX( res1 = diag.asDiagonal() * points.topRows<2>(), res2 = diag.asDiagonal() * tmp1 );
Matrix2d tmp2 = points.topLeftCorner<2,2>();
VERIFY_IS_APPROX(( res1 = points.topLeftCorner<2,2>()*diag.asDiagonal()) , res2 = tmp2*diag.asDiagonal() );
}
vd EigenInterface::eigenVectors(vd mm) {
Matrix2d m(2,2);
m(0,0) = mm[0];
m(1,0) = mm[1];
m(0,1) = mm[2];
m(1,1) = mm[3];
cout << m << endl;
VectorXcd eigenval = m.eigenvalues();
cout << "Eigenvalues: "<< endl;
cout << eigenval << endl;
cout << "Diagonal:" << endl;
cout << m.diagonal() << endl;
Vector2d w = {1,2};
Matrix2d mu = w.asDiagonal();
cout << "Diagonal matrix from: " << w << endl;
cout << mu << "||||\n";
//Jacobi decomposition: projecting-out the positive part
JacobiSVD<Matrix2d> svd(m, ComputeFullU | ComputeFullV);
auto sing = svd.singularValues();
Matrix2d U = svd.matrixU();
Vector2d sin = svd.singularValues();
cout << "Printing the vectof of singular values:\n";
cout << sin << endl;
cout << "-------------\n";
cout << "Printing U:\n";
cout << svd.matrixU() << endl;
cout << "Printing V:\n";
cout << svd.matrixU() << endl;
cout << "--------------\n";
EigenSolver<Matrix2d> roz(m);
cout << "Eigenvalues:\n" << roz.eigenvalues() << endl;
cout << "Eigenvectors:\n" << roz.eigenvectors() << endl;
// cout << "Singular values from decomposed m:" << wsing << endl;
//Type casting:
// Eigen::MatrixXd d; // Matrix of doubles.
// Eigen::MatrixXf f = d.cast <float> (); // Matrix of floats.
return vd();
}
