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diff --git a/src/Spatial_searching/test/test_Kd_tree_search.cpp b/src/Spatial_searching/test/test_Kd_tree_search.cpp
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+++ b/src/Spatial_searching/test/test_Kd_tree_search.cpp
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+/*    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):       Clement Jamin
+ *
+ *    Copyright (C) 2016  INRIA
+ *
+ *    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/>.
+ */
+
+#define BOOST_TEST_DYN_LINK
+#define BOOST_TEST_MODULE Spatial_searching - test Kd_tree_search
+#include <boost/test/unit_test.hpp>
+
+#include <gudhi/Kd_tree_search.h>
+
+#include <CGAL/Epick_d.h>
+#include <CGAL/Random.h>
+
+#include <vector>
+
+BOOST_AUTO_TEST_CASE(test_Kd_tree_search) {
+  typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
+  typedef K::FT FT;
+  typedef K::Point_d Point;
+  typedef std::vector<Point> Points;
+
+  typedef Gudhi::spatial_searching::Kd_tree_search<
+      K, Points> Points_ds;
+
+  CGAL::Random rd;
+
+  Points points;
+  for (int i = 0; i < 500; ++i)
+    points.push_back(Point(rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1), rd.get_double(-1., 1)));
+
+  Points_ds points_ds(points);
+
+  // Test query_k_nearest_neighbors
+  std::size_t closest_pt_index =
+      points_ds.query_k_nearest_neighbors(points[10], 1, false).begin()->first;
+  BOOST_CHECK(closest_pt_index == 10);
+
+  auto kns_range = points_ds.query_k_nearest_neighbors(points[20], 10, true);
+
+  std::vector<std::size_t> knn_result;
+  FT last_dist = -1.;
+  for (auto const& nghb : kns_range) {
+    BOOST_CHECK(nghb.second > last_dist);
+    knn_result.push_back(nghb.second);
+    last_dist = nghb.second;
+  }
+
+  // Test query_incremental_nearest_neighbors 
+  closest_pt_index =
+      points_ds.query_incremental_nearest_neighbors(points[10]).begin()->first;
+  BOOST_CHECK(closest_pt_index == 10);
+
+  auto inn_range = points_ds.query_incremental_nearest_neighbors(points[20]);
+
+  std::vector<std::size_t> inn_result;
+  last_dist = -1.;
+  auto inn_it = inn_range.begin();
+  for (int i = 0; i < 10; ++inn_it, ++i) {
+    auto const& nghb = *inn_it;
+    BOOST_CHECK(nghb.second > last_dist);
+    inn_result.push_back(nghb.second);
+    last_dist = nghb.second;
+  }
+
+  // Same result for KNN and INN?
+  BOOST_CHECK(knn_result == inn_result);
+
+  // Test query_k_farthest_neighbors
+  auto kfn_range = points_ds.query_k_farthest_neighbors(points[20], 10, true);
+
+  std::vector<std::size_t> kfn_result;
+  last_dist = kfn_range.begin()->second;
+  for (auto const& nghb : kfn_range) {
+    BOOST_CHECK(nghb.second <= last_dist);
+    kfn_result.push_back(nghb.second);
+    last_dist = nghb.second;
+  }
+
+  // Test query_k_farthest_neighbors
+  auto ifn_range = points_ds.query_incremental_farthest_neighbors(points[20]);
+
+  std::vector<std::size_t> ifn_result;
+  last_dist = ifn_range.begin()->second;
+  auto ifn_it = ifn_range.begin();
+  for (int i = 0; i < 10; ++ifn_it, ++i) {
+    auto const& nghb = *ifn_it;
+    BOOST_CHECK(nghb.second <= last_dist);
+    ifn_result.push_back(nghb.second);
+    last_dist = nghb.second;
+  }
+
+  // Same result for KFN and IFN?
+  BOOST_CHECK(kfn_result == ifn_result);
+}