From 9469df0b1d91e133ee246229fb5df597c9d18e5b Mon Sep 17 00:00:00 2001
From: skachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Wed, 15 Jun 2016 08:33:59 +0000
Subject: Added the forgotten Construct_closest_landmark_table.h

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1280 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 9ec56f3a9da0fc63a96fb9f7075bc533bfd18393
---
 .../gudhi/Construct_closest_landmark_table.h       | 90 ++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h

diff --git a/src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h b/src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h
new file mode 100644
index 00000000..ef711c34
--- /dev/null
+++ b/src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h
@@ -0,0 +1,90 @@
+/*    This file is part of the Gudhi Library. The Gudhi library
+ *    (Geometric Understanding in Higher Dimensions) is a generic C++
+ *    library for computational topology.
+ *
+ *    Author(s):       Siargey Kachanovich
+ *
+ *    Copyright (C) 2015  INRIA Sophia Antipolis-Méditerranée (France)
+ *
+ *    This program is free software: you can redistribute it and/or modify
+ *    it under the terms of the GNU General Public License as published by
+ *    the Free Software Foundation, either version 3 of the License, or
+ *    (at your option) any later version.
+ *
+ *    This program is distributed in the hope that it will be useful,
+ *    but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ *    GNU General Public License for more details.
+ *
+ *    You should have received a copy of the GNU General Public License
+ *    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#ifndef CONSTRUCT_CLOSEST_LANDMARK_TABLE_H_
+#define CONSTRUCT_CLOSEST_LANDMARK_TABLE_H_
+
+#include <boost/range/size.hpp>
+
+#include <queue>  // for priority_queue<>
+#include <utility>  // for pair<>
+#include <iterator>
+#include <vector>
+#include <set>
+
+namespace Gudhi {
+
+namespace witness_complex {
+
+  /**
+   *  \ingroup witness_complex
+   * \brief Construct the closest landmark tables for all witnesses.
+   *  \details Output a table 'knn', each line of which represents a witness and
+   *   consists of landmarks sorted by
+   *   euclidean distance from the corresponding witness.
+   *
+   *  The type WitnessContainer is a random access range and
+   *  the type LandmarkContainer is a range.
+   *  The type KNearestNeighbors can be seen as 
+   *  Witness_range<Closest_landmark_range<Vertex_handle>>, where
+   *  Witness_range and Closest_landmark_range are random access ranges and
+   *  Vertex_handle is the label type of a vertex in a simplicial complex.
+   *  Closest_landmark_range needs to have push_back operation.
+   */
+
+  template <typename WitnessContainer,
+            typename LandmarkContainer,
+            typename KNearestNeighbours>
+  void construct_closest_landmark_table(WitnessContainer const &points,
+                                        LandmarkContainer const &landmarks,
+                                        KNearestNeighbours &knn) {
+    int nbP = boost::size(points);
+    assert(nbP >= boost::size(landmarks));
+
+    int dim = boost::size(*std::begin(points));
+    typedef std::pair<double, int> dist_i;
+    typedef bool (*comp)(dist_i, dist_i);
+    knn = KNearestNeighbours(nbP);
+    for (int points_i = 0; points_i < nbP; points_i++) {
+      std::priority_queue<dist_i, std::vector<dist_i>, comp> l_heap([](dist_i j1, dist_i j2) {
+          return j1.first > j2.first;
+        });
+      typename LandmarkContainer::const_iterator landmarks_it;
+      int landmarks_i = 0;
+      for (landmarks_it = landmarks.begin(), landmarks_i = 0; landmarks_it != landmarks.end();
+           ++landmarks_it, landmarks_i++) {
+        dist_i dist = std::make_pair(euclidean_distance(points[points_i], *landmarks_it), landmarks_i);
+        l_heap.push(dist);
+      }
+      for (int i = 0; i < dim + 1; i++) {
+        dist_i dist = l_heap.top();
+        knn[points_i].push_back(dist.second);
+        l_heap.pop();
+      }
+    }
+  }
+
+}  // namespace witness_complex
+
+}  // namespace Gudhi
+
+#endif  // CONSTRUCT_CLOSEST_LANDMARK_TABLE_H_
-- 
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