static const Eigen::MatrixXcd executeEigenSolver(const QList< Analitza::Expression >& args, bool computeEigenvectors, QStringList &errors)
{
const int nargs = args.size();
if (nargs != 1) {
errors.append(QCoreApplication::tr("Invalid parameter count for '%1'. Should have 1 parameter").arg(EigenvaluesCommand::id));
return Eigen::MatrixXcd();
}
const Analitza::Matrix *matrix = static_cast<const Analitza::Matrix*>(args.first().tree());
if (!matrix->isSquare()) {
errors.append(QCoreApplication::tr("To use '%1' command the input matrix must be square").arg(EigenvaluesCommand::id));
return Eigen::MatrixXcd();
}
const int m = matrix->rowCount();
const int n = matrix->columnCount();
Eigen::MatrixXd realmatrix(m, n);
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
if (matrix->at(i,j)->type() == Analitza::Object::value) {
const Analitza::Cn *entry = static_cast<const Analitza::Cn*>(matrix->at(i,j));
const Analitza::Cn::ValueFormat entryformat = entry->format();
//Don't allow complex numbers
if (entryformat == Analitza::Cn::Char || entryformat == Analitza::Cn::Real ||
entryformat == Analitza::Cn::Integer || entryformat == Analitza::Cn::Boolean) {
realmatrix(i,j) = entry->value();
} else {
errors.append(QCoreApplication::tr("Invalid parameter type in matrix entry (%1,%2) for '%3', it must be a number value")
.arg(i).arg(j).arg(EigenvaluesCommand::id));
return Eigen::MatrixXcd();
}
} else {
errors.append(QCoreApplication::tr("Invalid parameter type in matrix entry (%1,%2) for '%3', it must be a number value")
.arg(i).arg(j).arg(EigenvaluesCommand::id));
return Eigen::MatrixXcd();
}
}
}
Q_ASSERT(nargs > 0);
Eigen::EigenSolver<Eigen::MatrixXd> eigensolver;
eigensolver.compute(realmatrix, computeEigenvectors);
Eigen::MatrixXcd ret;
if (computeEigenvectors)
ret = eigensolver.eigenvectors();
else
ret = eigensolver.eigenvalues();
return ret;
}
// full reorthogonalization
double EigenTriangle::solve(int maxIter)
{
int n = graph -> VertexCount();
srand(time(0));
// check if rows == cols()
if (maxIter > n)
maxIter = n;
vector<VectorXd> v;
v.push_back(VectorXd::Random(n));
v[0] = v[0] / v[0].norm();
VectorXd alpha(n);
VectorXd beta(n + 1);
beta[0] = 0;
VectorXd w;
int last = 0;
printf("%d\n", maxIter);
for (int i = 0; i < maxIter; i ++)
{
if (i * 100 / maxIter > last + 10 || i == maxIter - 1)
{
last = (i + 1) * 100 / maxIter;
std::cout << "\r" << "[EigenTriangle] Processing " << last << "% ..." << std::flush;
}
w = adjMatrix * v[i];
alpha[i] = w.dot(v[i]);
if (i == maxIter - 1)
break;
w -= alpha[i] * v[i];
if (i > 0)
w -= beta[i] * v[i - 1];
beta[i + 1] = w.norm();
v.push_back(w / beta[i + 1]);
for (int j = 0; j <= i; j ++)
{
v[i + 1] = v[i + 1] - v[i + 1].dot(v[j]) * v[j];
}
}
printf("\n");
int k = maxIter;
MatrixXd T = MatrixXd::Zero(k, k);
for (int i = 0; i < k; i ++)
{
T(i, i) = alpha[i];
if (i < k - 1)
T(i, i + 1) = beta[i + 1];
if (i > 0)
T(i, i - 1) = beta[i];
}
//std::cout << T << std::endl;
Eigen::EigenSolver<MatrixXd> eigenSolver;
eigenSolver.compute(T, /* computeEigenvectors = */ false);
Eigen::VectorXcd eigens = eigenSolver.eigenvalues();
double res = 0;
for (int i = 0; i < eigens.size(); i ++)
{
std::complex<double> tmp = eigens[i];
res += pow(tmp.real(), 3);
printf("%.5lf\n", tmp.real());
}
res /= 6;
return res;
}
// partial reorthogonalization
double EigenTriangle::solve(int maxIter, double tol)
{
int n = graph -> VertexCount();
srand(time(0));
if (maxIter > n)
maxIter = n;
double na = adjMatrix.norm();
double phi = tol * na;
double delta = tol * sqrt(na);
vector<Triplet<double> > omega_vector;
//MatrixXd omega = MatrixXd::Zero(n + 2, n + 2);
for (int i = 0; i < n + 2; i ++)
{
//omega(i, i - 1) = phi;
if (i > 0)
omega_vector.push_back(Triplet<double>(i, i - 1, phi));
omega_vector.push_back(Triplet<double>(i, i, 1));
}
omega.resize(n + 2, n + 2);
omega.setFromTriplets(omega_vector.begin(), omega_vector.end());
//std::cout << omega << std::endl;
vector<SparseVector<double> > v;
//v.push_back(SparseVector::Random(n));
SparseVector<double> firstV(n);
for (int i = 0; i < 100; i ++)
firstV.coeffRef(i % n) = i;
v.push_back(firstV);
v[0] /= v[0].norm();
VectorXd alpha(n);
VectorXd beta(n + 1);
beta[0] = 0;
SparseVector<double> w;
bool flag = false;
int num = 0; // reorthogonalization times
int last = -1;
for (int i = 0; i < maxIter; i ++)
{
if ((i + 1) * 100 / maxIter > last)
{
last = (i + 1) * 100 / maxIter;
std::cout << "\r" << "[EigenTriangle] Processing " << last << "% ..." << std::flush;
}
//printf("== Iter %d ===\n", i);
w = adjMatrix * v[i];
alpha.coeffRef(i) = w.dot(v[i]);
if (i == maxIter - 1)
break;
w -= alpha[i] * v[i];
if (i > 0)
w -= beta[i] * v[i - 1];
beta[i + 1] = w.norm();
v.push_back(w / beta[i + 1]);
if (flag)
{
flag = false;
for (int j = 0; j <= i; j ++)
v[i + 1] -= v[j].dot(v[i + 1]) * v[j];
for (int j = 0; j <= i; j ++)
omega.coeffRef(i + 1, j) = phi;
//for (int j = 0; j <= i; j ++)
// printf("%.5lf\n", v[i + 1].dot(v[j]));
}
else
{
omega.coeffRef(i + 1, 0) = 0.0;
if (i > 0)
{
omega.coeffRef(i + 1, 0) = 1.0 / beta(i) * ((alpha(0) - alpha(i)) * omega.coeffRef(i, 0) - beta(i - 1) * omega.coeffRef(i - 1, 0)) + delta;
}
for (int j = 1; j <= i; j ++)
{
omega.coeffRef(i + 1, j) = 1.0 / beta(i) * (beta(j) * omega.coeffRef(i, j + 1) + (alpha(j) - alpha(i)) * omega.coeffRef(i, j) - beta(j - 1) * omega.coeffRef(i, j - 1) - beta(i - 1) * omega.coeffRef(i - 1, j)) + delta;
}
}
double mx = 0.0;
for (int j = 0; j <= i; j ++)
if (mx < fabs(omega.coeffRef(i + 1, j)))
mx = fabs(omega.coeffRef(i + 1, j));
if (mx > sqrt(tol))
{
for (int j = 0; j <= i; j ++)
omega.coeffRef(i + 1, j) = phi;
num ++;
for (int j = 0; j <= i; j ++)
v[i + 1] -= v[i + 1].dot(v[j]) * v[j];
flag = true;
}
}
printf("\n");
int k = maxIter;
MatrixXd T = MatrixXd::Zero(k, k);
for (int i = 0; i < k; i ++)
{
T(i, i) = alpha[i];
if (i < k - 1)
T(i, i + 1) = beta[i + 1];
if (i > 0)
T(i, i - 1) = beta[i];
}
//std::cout << T << std::endl;
Eigen::EigenSolver<MatrixXd> eigenSolver;
eigenSolver.compute(T, false);
Eigen::VectorXcd eigens = eigenSolver.eigenvalues();
double res = 0;
for (int i = 0; i < eigens.size(); i ++)
{
std::complex<double> tmp = eigens[i];
res += pow(tmp.real(), 3) / 6;
std::cout << i << ": " << tmp << std::endl;
}
//res /= 6;
return res;
}
void
approx_relpose_generalized
(
const Eigen::Matrix<double,6,6> &w1,
const Eigen::Matrix<double,6,6> &w2,
const Eigen::Matrix<double,6,6> &w3,
const Eigen::Matrix<double,6,6> &w4,
const Eigen::Matrix<double,6,6> &w5,
const Eigen::Matrix<double,6,6> &w6,
std::vector<Eigen::Vector3d> &rsolns
)
{
const Eigen::Matrix<double,15,35> A = computeA(w1,w2,w3,w4,w5,w6);
Eigen::Matrix<double,15,35> gbA;
gbA << A.col(0),A.col(1),A.col(2),A.col(3),A.col(4),A.col(5),A.col(7),A.col(9),A.col(11),A.col(15),A.col(18),A.col(21),A.col(24),A.col(28),A.col(13),A.col(6),A.col(8),A.col(10),A.col(12),A.col(16),A.col(19),A.col(22),A.col(25),A.col(29),A.col(14),A.col(17),A.col(20),A.col(23),A.col(26),A.col(30),A.col(32),A.col(27),A.col(31),A.col(33),A.col(34);
const Eigen::Matrix<double,15,20> G = gbA.block<15,15>(0,0).lu().solve(gbA.block<15,20>(0,15));
Eigen::Matrix<double,20,20> M = Eigen::Matrix<double,20,20>::Zero();
M.block<10,20>(0,0) = -G.block<10,20>(5,0);
M(10,4) = 1;
M(11,5) = 1;
M(12,6) = 1;
M(13,7) = 1;
M(14,8) = 1;
M(15,9) = 1;
M(16,13) = 1;
M(17,14) = 1;
M(18,15) = 1;
M(19,18) = 1;
const Eigen::EigenSolver< Eigen::Matrix<double,20,20> > eigensolver(M,true);
const Eigen::EigenSolver< Eigen::Matrix<double,20,20> >::EigenvalueType evalues = eigensolver.eigenvalues();
const Eigen::EigenSolver< Eigen::Matrix<double,20,20> >::EigenvectorsType evecs = eigensolver.eigenvectors();
rsolns.clear();
rsolns.reserve(evalues.size());
for ( size_t i = 0; i < evalues.size(); i++ )
{
if ( evalues[i].imag() != 0 ) continue;
const double zsoln = evalues(i).real();
const double xsoln = evecs(16,i).real()/evecs(19,i).real();
const double ysoln = evecs(17,i).real()/evecs(19,i).real();
Eigen::Vector3d rsoln;
rsoln << xsoln, ysoln, zsoln;
rsolns.push_back(rsoln);
}
}