本文整理汇总了C++中StatisticsVector::begin方法的典型用法代码示例。如果您正苦于以下问题：C++ StatisticsVector::begin方法的具体用法？C++ StatisticsVector::begin怎么用？C++ StatisticsVector::begin使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类StatisticsVector的用法示例。
在下文中一共展示了StatisticsVector::begin方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: main
//.........这里部分代码省略.........
soln,
var_names);
/**
* Maybe Triangulate
*/
// if (dim == 2 && triangulate)
if (triangulate)
{
if (verbose)
libMesh::out << "...Converting to all simplices...\n";
MeshTools::Modification::all_tri(mesh);
}
/**
* Compute Shape quality metrics
*/
if (do_quality)
{
StatisticsVector<Real> sv;
sv.reserve(mesh.n_elem());
libMesh::out << "Quality type is: " << Quality::name(quality_type) << std::endl;
// What are the quality bounds for this element?
std::pair<Real, Real> bounds = mesh.elem(0)->qual_bounds(quality_type);
libMesh::out << "Quality bounds for this element type are: (" << bounds.first
<< ", " << bounds.second << ") "
<< std::endl;
MeshBase::const_element_iterator it = mesh.active_elements_begin(),
end = mesh.active_elements_end();
for (; it != end; ++it)
{
Elem *e = *it;
sv.push_back(e->quality(quality_type));
}
const unsigned int n_bins = 10;
libMesh::out << "Avg. shape quality: " << sv.mean() << std::endl;
// Find element indices below the specified cutoff.
// These might be considered "bad" elements which need refinement.
std::vector<dof_id_type> bad_elts = sv.cut_below(0.8);
libMesh::out << "Found " << bad_elts.size()
<< " of " << mesh.n_elem()
<< " elements below the cutoff." << std::endl;
/*
for (unsigned int i=0; i<bad_elts.size(); i++)
libMesh::out << bad_elts[i] << " ";
libMesh::out << std::endl;
*/
// Compute the histogram for this distribution
std::vector<dof_id_type> histogram;
sv.histogram(histogram, n_bins);
/*
for (unsigned int i=0; i<n_bins; i++)
histogram[i] = histogram[i] / mesh.n_elem();
*/
示例2: main
//.........这里部分代码省略.........
<< std::endl;
for (const auto & elem : mesh.active_element_ptr_range())
sv.push_back(elem->quality(quality_type));
const unsigned int n_bins = 10;
libMesh::out << "Avg. shape quality: " << sv.mean() << std::endl;
// Find element indices below the specified cutoff.
// These might be considered "bad" elements which need refinement.
std::vector<dof_id_type> bad_elts = sv.cut_below(0.8);
libMesh::out << "Found " << bad_elts.size()
<< " of " << mesh.n_elem()
<< " elements below the cutoff." << std::endl;
// Compute the histogram for this distribution
std::vector<dof_id_type> histogram;
sv.histogram(histogram, n_bins);
const bool do_matlab = true;
if (do_matlab)
{
std::ofstream out ("histo.m");
out << "% This is a sample histogram plot for Matlab." << std::endl;
out << "bin_members = [" << std::endl;
for (unsigned int i=0; i<n_bins; i++)
out << static_cast<Real>(histogram[i]) / static_cast<Real>(mesh.n_elem())
<< std::endl;
out << "];" << std::endl;
std::vector<Real> bin_coords(n_bins);
const Real max = *(std::max_element(sv.begin(), sv.end()));
const Real min = *(std::min_element(sv.begin(), sv.end()));
const Real delta = (max - min) / static_cast<Real>(n_bins);
for (unsigned int i=0; i<n_bins; i++)
bin_coords[i] = min + (i * delta) + delta / 2.0 ;
out << "bin_coords = [" << std::endl;
for (unsigned int i=0; i<n_bins; i++)
out << bin_coords[i] << std::endl;
out << "];" << std::endl;
out << "bar(bin_coords, bin_members, 1);" << std::endl;
out << "hold on" << std::endl;
out << "plot (bin_coords, 0, 'kx');" << std::endl;
out << "xlabel('Quality (0=Worst, 1=Best)');" << std::endl;
out << "ylabel('Percentage of elements in each bin');" << std::endl;
out << "axis([" << min << "," << max << ",0, max(bin_members)]);" << std::endl;
out << "title('" << Quality::name(quality_type) << "');" << std::endl;
}
}
// Possibly convert all linear elements to second-order
// counterparts
if (convert_first_order)
{
if (verbose)
libMesh::out << "Converting elements to first order counterparts\n";
mesh.all_first_order();
if (verbose)