songsenand
/
cgal
mirror of https://github.com/CGAL/cgal
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Comments clean-up

This commit is contained in:
Clement Jamin 2014-09-10 14:29:34 +02:00
parent 47a262697e
commit d37b81a25e
1 changed files with 4 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
Tangential_complex/include/CGAL/Tangential_complex.h
View File

@ -315,10 +315,6 @@ private:
center_vertex->data() = i;
//std::cerr << "Inserted CENTER POINT of weight " << CGAL::sqrt(max_squared_weight) << std::endl;
/*std::cerr << 0 << " "
<< 0 << " "
<< CGAL::sqrt(max_squared_weight) << std::endl;*/
// Insert the other points
std::vector<Tr_point>::const_iterator it_wp = projected_points.begin();
it_p = m_points.begin();
@ -342,16 +338,11 @@ private:
Tr_point wp = local_tr_traits.construct_weighted_point_d_object()(
local_tr_traits.point_drop_weight_d_object()(*it_wp),
w);
/*Tr_bare_point bp = traits.point_drop_weight_d_object()(*it_wp);
Tr_point wp(traits.point_drop_weight_d_object()(*it_wp), w);*/
Tr_vertex_handle vh = p_local_tr->insert_if_in_star(wp, center_vertex);
//Tr_vertex_handle vh = p_local_tr->insert(wp);
if (vh != Tr_vertex_handle())
{
/*std::cerr << traits.point_drop_weight_d_object()(*it_wp)[0] << " "
<< traits.point_drop_weight_d_object()(*it_wp)[1] << " "
<< w << std::endl;*/
vh->data() = j;
}
++it_wp;
@ -388,15 +379,16 @@ private:
//******************************* PCA *************************************
const int amb_dim = Ambient_dimension<Point>::value;
Eigen::MatrixXd mat(NUM_POINTS_FOR_PCA, amb_dim);
// One row = one point
Eigen::MatrixXd mat_points(NUM_POINTS_FOR_PCA, amb_dim);
int j = 0;
for (Sorted_points::const_iterator it = sorted_points.begin() ;
j < NUM_POINTS_FOR_PCA ; ++it, ++j)
{
for (int i = 0 ; i < amb_dim ; ++i)
mat(j, i) = (*it)[i];
mat_points(j, i) = (*it)[i];
}
Eigen::MatrixXd centered = mat.rowwise() - mat.colwise().mean();
Eigen::MatrixXd centered = mat_points.rowwise() - mat_points.colwise().mean();
Eigen::MatrixXd cov = centered.adjoint() * centered;
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> eig(cov);