C++ (Cpp) SelfAdjointEigenSolver::info Examples

Example #1

File: nurbs_tools.cpp Project: VictorLamoine/pcl

void
NurbsTools::pca (const vector_vec2d &data, Eigen::Vector2d &mean, Eigen::Matrix2d &eigenvectors,
                 Eigen::Vector2d &eigenvalues)
{
  if (data.empty ())
  {
    printf ("[NurbsTools::pca] Error, data is empty\n");
    abort ();
  }

  mean = computeMean (data);

  unsigned s = unsigned (data.size ());

  Eigen::MatrixXd Q (2, s);

  for (unsigned i = 0; i < s; i++)
    Q.col (i) << (data[i] - mean);

  Eigen::Matrix2d C = Q * Q.transpose ();

  Eigen::SelfAdjointEigenSolver<Eigen::Matrix2d> eigensolver (C);
  if (eigensolver.info () != Success)
  {
    printf ("[NurbsTools::pca] Can not find eigenvalues.\n");
    abort ();
  }

  for (int i = 0; i < 2; ++i)
  {
    eigenvalues (i) = eigensolver.eigenvalues () (1 - i);
    eigenvectors.col (i) = eigensolver.eigenvectors ().col (1 - i);
  }
}

Example #2

File: MomentTools.cpp Project: hmazhar/scisim

static void diagonalizeInertiaTensor( const Matrix3s& I, Matrix3s& R0, Vector3s& I0 )
{
  // Inertia tensor should by symmetric
  assert( ( I - I.transpose() ).lpNorm<Eigen::Infinity>() <= 1.0e-6 );
  // Inertia tensor should have positive determinant
  assert( I.determinant() > 0.0 );

  // Compute the eigenvectors and eigenvalues of the input matrix
  const Eigen::SelfAdjointEigenSolver<Matrix3s> es{ I };

  // Check for errors
  if( es.info() == Eigen::NumericalIssue )
  {
    std::cerr << "Warning, failed to compute eigenvalues of inertia tensor due to Eigen::NumericalIssue" << std::endl;
  }
  else if( es.info() == Eigen::NoConvergence )
  {
    std::cerr << "Warning, failed to compute eigenvalues of inertia tensor due to Eigen::NoConvergence" << std::endl;
  }
  else if( es.info() == Eigen::InvalidInput )
  {
    std::cerr << "Warning, failed to compute eigenvalues of inertia tensor due to Eigen::InvalidInput" << std::endl;
  }
  assert( es.info() == Eigen::Success );

  // Save the eigenvectors and eigenvalues
  I0 = es.eigenvalues();
  assert( ( I0.array() > 0.0 ).all() );
  assert( I0.x() <= I0.y() );
  assert( I0.y() <= I0.z() );
  R0 = es.eigenvectors();
  assert( fabs( fabs( R0.determinant() ) - 1.0 ) <= 1.0e-6 );

  // Ensure that we have an orientation preserving transform
  if( R0.determinant() < 0.0 )
  {
    R0.col( 0 ) *= -1.0;
  }
}

Example #3

File: opennurbs_fit.cpp Project: cogitokat/brlcad

void
NurbsTools::pca (const vector_vec3d &data, ON_3dVector &mean, Eigen::Matrix3d &eigenvectors,
		 Eigen::Vector3d &eigenvalues)
{
  if (data.empty ())
  {
    printf ("[NurbsTools::pca] Error, data is empty\n");
    abort ();
  }

  mean = computeMean (data);

  unsigned s = data.size ();

  ON_Matrix Q(3, s);

  for (unsigned i = 0; i < s; i++) {
    Q[0][i] = data[i].x - mean.x;
    Q[1][i] = data[i].y - mean.y;
    Q[2][i] = data[i].z - mean.z;
  }

  ON_Matrix Qt = Q;
  Qt.Transpose();

  ON_Matrix oC;
  oC.Multiply(Q,Qt);

  Eigen::Matrix3d C(3,3);
  for (unsigned i = 0; i < 3; i++) {
      for (unsigned j = 0; j < 3; j++) {
	  C(i,j) = oC[i][j];
      }
  }

  Eigen::SelfAdjointEigenSolver < Eigen::Matrix3d > eigensolver (C);
  if (eigensolver.info () != Eigen::Success)
  {
    printf ("[NurbsTools::pca] Can not find eigenvalues.\n");
    abort ();
  }

  for (int i = 0; i < 3; ++i)
  {
    eigenvalues (i) = eigensolver.eigenvalues () (2 - i);
    if (i == 2)
      eigenvectors.col (2) = eigenvectors.col (0).cross (eigenvectors.col (1));
    else
      eigenvectors.col (i) = eigensolver.eigenvectors ().col (2 - i);
  }
}