/**
* <p>
* Puts all the real eigenvectors into the columns of a matrix. If an eigenvalue is imaginary
* then the corresponding eigenvector will have zeros in its column.
* </p>
*
* @param eig An eigenvalue decomposition which has already decomposed a matrix.
* @return An m by m matrix containing eigenvectors in its columns.
*/
public static DMatrixRMaj createMatrixV(EigenDecomposition_F64 <DMatrixRMaj> eig)
{
int N = eig.getNumberOfEigenvalues();
DMatrixRMaj V = new DMatrixRMaj(N, N);
for (int i = 0; i < N; i++)
{
Complex_F64 c = eig.getEigenvalue(i);
if (c.isReal())
{
DMatrixRMaj v = eig.getEigenVector(i);
if (v != null)
{
for (int j = 0; j < N; j++)
{
V.set(j, i, v.get(j, 0));
}
}
}
}
return(V);
}
public bool extractVectors(DMatrixRMaj Q_h)
{
Array.Clear(eigenvectorTemp.data, 0, eigenvectorTemp.data.Length);
// extract eigenvectors from the shur matrix
// start at the top left corner of the matrix
bool triangular = true;
for (int i = 0; i < N; i++)
{
Complex_F64 c = _implicit.eigenvalues[N - i - 1];
if (triangular && !c.isReal())
{
triangular = false;
}
if (c.isReal() && eigenvectors[N - i - 1] == null)
{
solveEigenvectorDuplicateEigenvalue(c.real, i, triangular);
}
}
// translate the eigenvectors into the frame of the original matrix
if (Q_h != null)
{
DMatrixRMaj temp = new DMatrixRMaj(N, 1);
for (int i = 0; i < N; i++)
{
DMatrixRMaj v = eigenvectors[i];
if (v != null)
{
CommonOps_DDRM.mult(Q_h, v, temp);
eigenvectors[i] = temp;
temp = v;
}
}
}
return(true);
}
private void checkSplitPerformImplicit()
{
// check for splits
for (int i = x2; i > x1; i--)
{
if (_implicit.isZero(i, i - 1))
{
x1 = i;
splits[numSplits++] = i - 1;
// reduce the scope of what it is looking at
return;
}
}
// first try using known eigenvalues in the same order they were originally found
if (onscript)
{
if (_implicit.steps > _implicit.exceptionalThreshold / 2)
{
onscript = false;
}
else
{
Complex_F64 a = origEigenvalues[indexVal];
// if no splits are found perform an implicit step
if (a.isReal())
{
_implicit.performImplicitSingleStep(x1, x2, a.getReal());
}
else if (x2 - x1 >= 1 && x1 + 2 < N)
{
_implicit.performImplicitDoubleStep(x1, x2, a.real, a.imaginary);
}
else
{
onscript = false;
}
}
}
else
{
// that didn't work so try a modified order
if (x2 - x1 >= 1 && x1 + 2 < N)
{
_implicit.implicitDoubleStep(x1, x2);
}
else
{
_implicit.performImplicitSingleStep(x1, x2, _implicit.A.get(x2, x2));
}
}
}
/**
* <p>
* A diagonal matrix where real diagonal element contains a real eigenvalue. If an eigenvalue
* is imaginary then zero is stored in its place.
* </p>
*
* @param eig An eigenvalue decomposition which has already decomposed a matrix.
* @return A diagonal matrix containing the eigenvalues.
*/
public static DMatrixRMaj createMatrixD(EigenDecomposition_F64 <DMatrixRMaj> eig)
{
int N = eig.getNumberOfEigenvalues();
DMatrixRMaj D = new DMatrixRMaj(N, N);
for (int i = 0; i < N; i++)
{
Complex_F64 c = eig.getEigenvalue(i);
if (c.isReal())
{
D.set(i, i, c.real);
}
}
return(D);
}
private void solveEigenvectorDuplicateEigenvalue(double real, int first, bool isTriangle)
{
double scale = Math.Abs(real);
if (scale == 0)
{
scale = 1;
}
eigenvectorTemp.reshape(N, 1, false);
eigenvectorTemp.zero();
if (first > 0)
{
if (isTriangle)
{
solveUsingTriangle(real, first, eigenvectorTemp);
}
else
{
solveWithLU(real, first, eigenvectorTemp);
}
}
eigenvectorTemp.reshape(N, 1, false);
for (int i = first; i < N; i++)
{
Complex_F64 c = _implicit.eigenvalues[N - i - 1];
if (c.isReal() && Math.Abs(c.real - real) / scale < 100.0 * UtilEjml.EPS)
{
eigenvectorTemp.data[i] = 1;
DMatrixRMaj v = new DMatrixRMaj(N, 1);
CommonOps_DDRM.multTransA(Q, eigenvectorTemp, v);
eigenvectors[N - i - 1] = v;
NormOps_DDRM.normalizeF(v);
eigenvectorTemp.data[i] = 0;
}
}
}
