Document the right function

1 files changed, 20 insertions, 19 deletions

diff --git a/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h b/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h
index 5fb8b588..63f747bf 100644
--- a/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h
+++ b/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h
@@ -34,7 +34,7 @@ namespace Gudhi {
 namespace collapse {
 
 /** \private
- * 
+ *
  * \brief Flag complex sparse matrix data structure.
  *
  * \tparam Vertex type must be an integer type.
@@ -279,24 +279,6 @@ end_move:
 
 };
 
-/** \brief Implicitly constructs a flag complex from edges as an input, collapses edges while preserving the persistent
- * homology and returns the remaining edges as a range. The filtration value of vertices is irrelevant to this function.
- *
- * \param[in] edges Range of Filtered edges. There is no need for the range to be sorted, as it will be done internally.
- *
- * \tparam FilteredEdgeRange Range of `std::tuple<Vertex_handle, Vertex_handle, Filtration_value>`
- * where `Vertex_handle` is the type of a vertex index.
- *
- * \return Remaining edges after collapse as a range of
- * `std::tuple<Vertex_handle, Vertex_handle, Filtration_value>`.
- * 
- * \ingroup edge_collapse
- * 
- * \note
- * Advanced: Defining the macro GUDHI_COLLAPSE_USE_DENSE_ARRAY tells gudhi to allocate a square table of size the
- * maximum vertex index. This usually speeds up the computation for dense graphs. However, for sparse graphs, the memory
- * use may be problematic and initializing this large table may be slow.
- */
 template<class FilteredEdgeRange, class Delay> auto flag_complex_collapse_edges(FilteredEdgeRange&& edges, Delay&&delay) {
   // Would it help to label the points according to some spatial sorting?
   auto first_edge_itr = std::begin(edges);
@@ -316,6 +298,25 @@ template<class FilteredEdgeRange, class Delay> auto flag_complex_collapse_edges(
   }
   return std::vector<typename Edge_collapser::Filtered_edge>();
 }
+
+/** \brief Implicitly constructs a flag complex from edges as an input, collapses edges while preserving the persistent
+ * homology and returns the remaining edges as a range. The filtration value of vertices is irrelevant to this function.
+ *
+ * \param[in] edges Range of Filtered edges. There is no need for the range to be sorted, as it will be done internally.
+ *
+ * \tparam FilteredEdgeRange Range of `std::tuple<Vertex_handle, Vertex_handle, Filtration_value>`
+ * where `Vertex_handle` is the type of a vertex index.
+ *
+ * \return Remaining edges after collapse as a range of
+ * `std::tuple<Vertex_handle, Vertex_handle, Filtration_value>`.
+ *
+ * \ingroup edge_collapse
+ *
+ * \note
+ * Advanced: Defining the macro GUDHI_COLLAPSE_USE_DENSE_ARRAY tells gudhi to allocate a square table of size the
+ * maximum vertex index. This usually speeds up the computation for dense graphs. However, for sparse graphs, the memory
+ * use may be problematic and initializing this large table may be slow.
+ */
 template<class FilteredEdgeRange> auto flag_complex_collapse_edges(const FilteredEdgeRange& edges) {
   return flag_complex_collapse_edges(edges, [](auto const&d){return d;});
 }