From 0a2844e38d40bebb0825f7833f14f434635628ae Mon Sep 17 00:00:00 2001
From: cjamin <cjamin@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Wed, 29 Jun 2016 16:11:49 +0000
Subject: Doc + new param (epsilon)

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1355 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: b2e70ef17cac8ca0f9dfaab430349468b59a60f5
---
 .../include/gudhi/Spatial_tree_data_structure.h    | 29 +++++++++++++++++++---
 1 file changed, 25 insertions(+), 4 deletions(-)

(limited to 'src/Spatial_searching')

diff --git a/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h b/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h
index e633bfc0..5153720b 100644
--- a/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h
+++ b/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h
@@ -36,6 +36,26 @@
 namespace Gudhi {
 namespace spatial_searching {
 
+
+  /**
+  * \class Spatial_tree_data_structure Spatial_tree_data_structure.h gudhi/Spatial_tree_data_structure.h
+  * \brief Spatial tree data structure to perform (approximate) nearest neighbor search.
+  *
+  * \ingroup spatial_searching
+  *
+  * \details
+  *  The class Spatial_tree_data_structure represents a tree-based data structure, based on .
+  *  <a target="_blank" href="http://doc.cgal.org/latest/Spatial_searching/index.html">CGAL dD spatial searching data structures</a>.
+  *  It provides a simplified API to perform (approximate) nearest neighbor searches. Contrary to CGAL default behavior, the tree
+  * does not store the points themselves, but stores indices.
+  *
+  * \tparam K requires a <a target="_blank"
+  *   href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class, which
+  *   can be static if you know the ambiant dimension at compile-time, or dynamic if you don't.
+  * \tparam Point_container_ is the type of the container where points are stored (on the user side).
+  *   It must provide random-access via `operator[]` and the points should be stored contiguously in memory.
+  *   `std::vector` is a good candidate.
+  */
 template <typename K, typename Point_container_>
 class Spatial_tree_data_structure
 {
@@ -126,7 +146,8 @@ public:
   KNS_range query_ANN(const
     Point &sp,
     unsigned int k,
-    bool sorted = true) const
+    bool sorted = true,
+    FT eps = FT(0)) const
   {
     // Initialize the search structure, and search all N points
     // Note that we need to pass the Distance explicitly since it needs to
@@ -135,7 +156,7 @@ public:
       m_tree,
       sp,
       k,
-      FT(0),
+      eps,
       true,
       CGAL::Distance_adapter<std::ptrdiff_t,Point*,CGAL::Euclidean_distance<Traits_base> >(
         (Point*)&(m_points[0])),
@@ -144,7 +165,7 @@ public:
     return search;
   }
 
-  INS_range query_incremental_ANN(const Point &sp) const
+  INS_range query_incremental_ANN(const Point &sp, FT eps = FT(0)) const
   {
     // Initialize the search structure, and search all N points
     // Note that we need to pass the Distance explicitly since it needs to
@@ -152,7 +173,7 @@ public:
     Incremental_neighbor_search search(
       m_tree,
       sp,
-      FT(0),
+      eps,
       true,
       CGAL::Distance_adapter<std::ptrdiff_t, Point*, CGAL::Euclidean_distance<Traits_base> >(
         (Point*)&(m_points[0])) );
-- 
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