From 0a071114ad08d2ce149f8b484dd8ff1b96b61fb1 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 9 Nov 2020 22:55:00 +0100
Subject: Don't test the equality case in sparsify_point_set. sqrt.

---
 src/Subsampling/include/gudhi/sparsify_point_set.h |  6 ++++--
 src/python/gudhi/subsampling.pyx                   |  2 +-
 src/python/test/test_subsampling.py                | 16 ++++++++++------
 3 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h
index 78e0da4a..afa6d45a 100644
--- a/src/Subsampling/include/gudhi/sparsify_point_set.h
+++ b/src/Subsampling/include/gudhi/sparsify_point_set.h
@@ -29,7 +29,7 @@ namespace subsampling {
  *  \ingroup subsampling
  *  \brief Outputs a subset of the input points so that the 
  *         squared distance between any two points
- *         is greater than or equal to `min_squared_dist`.
+ *         is greater than `min_squared_dist`.
  *
  * \tparam Kernel must be a model of the <a target="_blank"
  *   href="http://doc.cgal.org/latest/Spatial_searching/classSearchTraits.html">SearchTraits</a>
@@ -53,6 +53,7 @@ sparsify_point_set(
                    OutputIterator output_it) {
   typedef typename Gudhi::spatial_searching::Kd_tree_search<
       Kernel, Point_range> Points_ds;
+  using std::sqrt;
 
 #ifdef GUDHI_SUBSAMPLING_PROFILING
   Gudhi::Clock t;
@@ -73,7 +74,8 @@ sparsify_point_set(
 
     // If another point Q is closer that min_squared_dist, mark Q to be dropped
     auto drop = [&dropped_points] (std::ptrdiff_t neighbor_point_idx) { dropped_points[neighbor_point_idx] = true; };
-    points_ds.all_near_neighbors(pt, min_squared_dist, boost::make_function_output_iterator(std::ref(drop)));
+    // FIXME: what if FT does not support sqrt?
+    points_ds.all_near_neighbors(pt, sqrt(min_squared_dist), boost::make_function_output_iterator(std::ref(drop)));
   }
 
 #ifdef GUDHI_SUBSAMPLING_PROFILING
diff --git a/src/python/gudhi/subsampling.pyx b/src/python/gudhi/subsampling.pyx
index b11d07e5..46f32335 100644
--- a/src/python/gudhi/subsampling.pyx
+++ b/src/python/gudhi/subsampling.pyx
@@ -105,7 +105,7 @@ def pick_n_random_points(points=None, off_file='', nb_points=0):
 
 def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0):
     """Outputs a subset of the input points so that the squared distance
-    between any two points is greater than or equal to min_squared_dist.
+    between any two points is greater than min_squared_dist.
 
     :param points: The input point set.
     :type points: Iterable[Iterable[float]]
diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py
index 31f64e32..4019852e 100755
--- a/src/python/test/test_subsampling.py
+++ b/src/python/test/test_subsampling.py
@@ -141,12 +141,16 @@ def test_simple_sparsify_points():
     # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 0.0) == []
     # assert gudhi.sparsify_point_set(points = [], min_squared_dist = 10.0) == []
     assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.0) == point_set
-    assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.0) == point_set
-    assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.0) == [
+    assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=0.999) == point_set
+    assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.001) == [
         [0, 1],
         [1, 0],
     ]
-    assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.01) == [[0, 1]]
+    assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=1.999) == [
+        [0, 1],
+        [1, 0],
+    ]
+    assert gudhi.sparsify_point_set(points=point_set, min_squared_dist=2.001) == [[0, 1]]
 
     assert (
         len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=0.0))
@@ -157,11 +161,11 @@ def test_simple_sparsify_points():
         == 5
     )
     assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.0))
+        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=40.1))
         == 4
     )
     assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=90.0))
+        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=89.9))
         == 3
     )
     assert (
@@ -169,7 +173,7 @@ def test_simple_sparsify_points():
         == 2
     )
     assert (
-        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=325.0))
+        len(gudhi.sparsify_point_set(off_file="subsample.off", min_squared_dist=324.9))
         == 2
     )
     assert (
-- 
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