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-rw-r--r--src/Subsampling/doc/Intro_subsampling.h2
-rw-r--r--src/Subsampling/example/CMakeLists.txt22
-rw-r--r--src/Subsampling/example/example_choose_n_farthest_points.cpp16
-rw-r--r--src/Subsampling/example/example_pick_n_random_points.cpp16
-rw-r--r--src/Subsampling/example/example_sparsify_point_set.cpp16
-rw-r--r--src/Subsampling/include/gudhi/choose_n_farthest_points.h149
-rw-r--r--src/Subsampling/include/gudhi/pick_n_random_points.h74
-rw-r--r--src/Subsampling/include/gudhi/sparsify_point_set.h42
-rw-r--r--src/Subsampling/test/CMakeLists.txt20
-rw-r--r--src/Subsampling/test/test_choose_n_farthest_points.cpp9
-rw-r--r--src/Subsampling/test/test_sparsify_point_set.cpp4
11 files changed, 179 insertions, 191 deletions
diff --git a/src/Subsampling/doc/Intro_subsampling.h b/src/Subsampling/doc/Intro_subsampling.h
index f8eb2fdd..c84616dd 100644
--- a/src/Subsampling/doc/Intro_subsampling.h
+++ b/src/Subsampling/doc/Intro_subsampling.h
@@ -34,7 +34,7 @@ namespace subsampling {
*
* @{
*
- * \section introduction Introduction
+ * \section subsamplingintroduction Introduction
*
* This Gudhi component offers methods to subsample a set of points.
*
diff --git a/src/Subsampling/example/CMakeLists.txt b/src/Subsampling/example/CMakeLists.txt
index 81b39d6e..54349f0c 100644
--- a/src/Subsampling/example/CMakeLists.txt
+++ b/src/Subsampling/example/CMakeLists.txt
@@ -3,20 +3,18 @@ project(Subsampling_examples)
if(CGAL_FOUND)
if (NOT CGAL_VERSION VERSION_LESS 4.8.1)
- message(STATUS "CGAL version: ${CGAL_VERSION}.")
-
if (EIGEN3_FOUND)
+ add_executable(Subsampling_example_pick_n_random_points example_pick_n_random_points.cpp)
+ add_executable(Subsampling_example_choose_n_farthest_points example_choose_n_farthest_points.cpp)
+ add_executable(Subsampling_example_sparsify_point_set example_sparsify_point_set.cpp)
+ target_link_libraries(Subsampling_example_sparsify_point_set ${CGAL_LIBRARY})
- add_executable(Subsampling_example_pick_n_random_points example_pick_n_random_points.cpp)
- add_executable(Subsampling_example_choose_n_farthest_points example_choose_n_farthest_points.cpp)
- add_executable(Subsampling_example_sparsify_point_set example_sparsify_point_set.cpp)
- target_link_libraries(Subsampling_example_sparsify_point_set ${CGAL_LIBRARY})
- else()
- message(WARNING "Eigen3 not found. Version 3.1.0 is required for Subsampling feature.")
+ add_test(Subsampling_example_pick_n_random_points
+ ${CMAKE_CURRENT_BINARY_DIR}/Subsampling_example_pick_n_random_points)
+ add_test(Subsampling_example_choose_n_farthest_points
+ ${CMAKE_CURRENT_BINARY_DIR}/Subsampling_example_choose_n_farthest_points)
+ add_test(Subsampling_example_sparsify_point_set
+ ${CMAKE_CURRENT_BINARY_DIR}/Subsampling_example_sparsify_point_set)
endif()
- else()
- message(WARNING "CGAL version: ${CGAL_VERSION} is too old to compile Subsampling examples. Version 4.8.1 is required.")
endif ()
-else()
- message(WARNING "CGAL not found. It is required for the Subsampling examples.")
endif()
diff --git a/src/Subsampling/example/example_choose_n_farthest_points.cpp b/src/Subsampling/example/example_choose_n_farthest_points.cpp
index 9955d0ec..533aba74 100644
--- a/src/Subsampling/example/example_choose_n_farthest_points.cpp
+++ b/src/Subsampling/example/example_choose_n_farthest_points.cpp
@@ -3,21 +3,19 @@
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
-#include <array>
#include <vector>
#include <iterator>
-int main (void)
-{
- typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
- typedef typename K::FT FT;
- typedef typename K::Point_d Point_d;
-
+int main(void) {
+ typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
+ typedef typename K::Point_d Point_d;
+
CGAL::Random rd;
std::vector<Point_d> points;
- for (int i = 0 ; i < 500 ; ++i)
- points.push_back(Point_d(std::array<FT,4>({rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1)})));
+ for (int i = 0; i < 500; ++i)
+ points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1),
+ rd.get_double(-1., 1), rd.get_double(-1., 1)));
K k;
std::vector<Point_d> results;
diff --git a/src/Subsampling/example/example_pick_n_random_points.cpp b/src/Subsampling/example/example_pick_n_random_points.cpp
index d2f063ba..1e38e405 100644
--- a/src/Subsampling/example/example_pick_n_random_points.cpp
+++ b/src/Subsampling/example/example_pick_n_random_points.cpp
@@ -3,21 +3,19 @@
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
-#include <array>
#include <vector>
#include <iterator>
-int main (void)
-{
- typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
- typedef typename K::FT FT;
- typedef typename K::Point_d Point_d;
-
+int main(void) {
+ typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
+ typedef typename K::Point_d Point_d;
+
CGAL::Random rd;
std::vector<Point_d> points;
- for (int i = 0 ; i < 500 ; ++i)
- points.push_back(Point_d(std::array<FT,4>({rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1)})));
+ for (int i = 0; i < 500; ++i)
+ points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1),
+ rd.get_double(-1., 1), rd.get_double(-1., 1)));
K k;
std::vector<Point_d> results;
diff --git a/src/Subsampling/example/example_sparsify_point_set.cpp b/src/Subsampling/example/example_sparsify_point_set.cpp
index cb5ccf0b..b35a18d9 100644
--- a/src/Subsampling/example/example_sparsify_point_set.cpp
+++ b/src/Subsampling/example/example_sparsify_point_set.cpp
@@ -3,21 +3,19 @@
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
-#include <array>
#include <vector>
#include <iterator>
-int main (void)
-{
- typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
- typedef typename K::FT FT;
- typedef typename K::Point_d Point_d;
-
+int main(void) {
+ typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
+ typedef typename K::Point_d Point_d;
+
CGAL::Random rd;
std::vector<Point_d> points;
- for (int i = 0 ; i < 500 ; ++i)
- points.push_back(Point_d(std::array<FT,4>({rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1)})));
+ for (int i = 0; i < 500; ++i)
+ points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1),
+ rd.get_double(-1., 1), rd.get_double(-1., 1)));
K k;
std::vector<Point_d> results;
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index 8bfb38a4..40c7808d 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -37,89 +37,86 @@
#include <algorithm> // for sort
#include <vector>
#include <random>
+#include <limits> // for numeric_limits<>
namespace Gudhi {
namespace subsampling {
- /**
- * \ingroup subsampling
- * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
- * current chosen point set to the subsampling.
- * The iteration starts with the landmark `starting point`.
- * \details It chooses `final_size` points from a random access range `input_pts` and
- * outputs it in the output iterator `output_it`.
- *
- */
-
- template < typename Kernel,
- typename Point_container,
- typename OutputIterator>
- void choose_n_farthest_points( Kernel const &k,
- Point_container const &input_pts,
- unsigned final_size,
- unsigned starting_point,
- OutputIterator output_it)
- {
- typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
-
- int nb_points = boost::size(input_pts);
- assert(nb_points >= final_size);
-
- unsigned current_number_of_landmarks = 0; // counter for landmarks
+
+/**
+ * \ingroup subsampling
+ * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
+ * current chosen point set to the subsampling.
+ * The iteration starts with the landmark `starting point`.
+ * \details It chooses `final_size` points from a random access range `input_pts` and
+ * outputs it in the output iterator `output_it`.
+ *
+ */
+template < typename Kernel,
+typename Point_container,
+typename OutputIterator>
+void choose_n_farthest_points(Kernel const &k,
+ Point_container const &input_pts,
+ std::size_t final_size,
+ std::size_t starting_point,
+ OutputIterator output_it) {
+ typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
+
+ std::size_t nb_points = boost::size(input_pts);
+ assert(nb_points >= final_size);
+
+ std::size_t current_number_of_landmarks = 0; // counter for landmarks
+ const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry)
+ std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from input_pts
+
+ std::size_t curr_max_w = starting_point;
+
+ for (current_number_of_landmarks = 0; current_number_of_landmarks != final_size; current_number_of_landmarks++) {
+ // curr_max_w at this point is the next landmark
+ *output_it++ = input_pts[curr_max_w];
+ std::size_t i = 0;
+ for (auto& p : input_pts) {
+ double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w));
+ if (curr_dist < dist_to_L[i])
+ dist_to_L[i] = curr_dist;
+ ++i;
+ }
+ // choose the next curr_max_w
double curr_max_dist = 0; // used for defining the furhest point from L
- const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry)
- std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from input_pts
-
- int curr_max_w = starting_point;
-
- for (current_number_of_landmarks = 0; current_number_of_landmarks != final_size; current_number_of_landmarks++) {
- // curr_max_w at this point is the next landmark
- *output_it++ = input_pts[curr_max_w];
- // std::cout << curr_max_w << "\n";
- unsigned i = 0;
- for (auto& p : input_pts) {
- double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w));
- if (curr_dist < dist_to_L[i])
- dist_to_L[i] = curr_dist;
- ++i;
+ for (i = 0; i < dist_to_L.size(); i++)
+ if (dist_to_L[i] > curr_max_dist) {
+ curr_max_dist = dist_to_L[i];
+ curr_max_w = i;
}
- // choose the next curr_max_w
- curr_max_dist = 0;
- for (i = 0; i < dist_to_L.size(); i++)
- if (dist_to_L[i] > curr_max_dist) {
- curr_max_dist = dist_to_L[i];
- curr_max_w = i;
- }
- }
- }
-
- /**
- * \ingroup subsampling
- * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
- * current chosen point set to the subsampling.
- * The iteration starts with a random landmark.
- * \details It chooses `final_size` points from a random access range `input_pts` and
- * outputs it in the output iterator `output_it`.
- *
- */
- template < typename Kernel,
- typename Point_container,
- typename OutputIterator>
- void choose_n_farthest_points( Kernel& k,
- Point_container const &input_pts,
- int final_size,
- OutputIterator output_it)
- {
- // Choose randomly the first landmark
- std::random_device rd;
- std::mt19937 gen(rd());
- std::uniform_int_distribution<> dis(1, 6);
- int starting_point = dis(gen);
- choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
}
-
-} // namespace subsampling
-
+}
+
+/**
+ * \ingroup subsampling
+ * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
+ * current chosen point set to the subsampling.
+ * The iteration starts with a random landmark.
+ * \details It chooses `final_size` points from a random access range `input_pts` and
+ * outputs it in the output iterator `output_it`.
+ *
+ */
+template < typename Kernel,
+typename Point_container,
+typename OutputIterator>
+void choose_n_farthest_points(Kernel const& k,
+ Point_container const &input_pts,
+ unsigned final_size,
+ OutputIterator output_it) {
+ // Choose randomly the first landmark
+ std::random_device rd;
+ std::mt19937 gen(rd());
+ std::uniform_int_distribution<> dis(1, 6);
+ int starting_point = dis(gen);
+ choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
+}
+
+} // namespace subsampling
+
} // namespace Gudhi
#endif // CHOOSE_N_FARTHEST_POINTS_H_
diff --git a/src/Subsampling/include/gudhi/pick_n_random_points.h b/src/Subsampling/include/gudhi/pick_n_random_points.h
index 4ca1fafc..e89b2b2d 100644
--- a/src/Subsampling/include/gudhi/pick_n_random_points.h
+++ b/src/Subsampling/include/gudhi/pick_n_random_points.h
@@ -20,63 +20,65 @@
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
-#ifndef PICK_RANDOM_POINTS_H_
-#define PICK_RANDOM_POINTS_H_
+#ifndef PICK_N_RANDOM_POINTS_H_
+#define PICK_N_RANDOM_POINTS_H_
+
+#include <gudhi/Clock.h>
#include <boost/range/size.hpp>
+#include <cstddef>
#include <random> // random_device, mt19937
#include <algorithm> // shuffle
#include <numeric> // iota
#include <iterator>
-#include <gudhi/Clock.h>
+#include <vector>
namespace Gudhi {
namespace subsampling {
-
- /**
- * \ingroup subsampling
- * \brief Subsample a point set by picking random vertices.
- *
- * \details It chooses `final_size` distinct points from a random access range `points`
- * and outputs them to the output iterator `output_it`.
- * Point_container::iterator should be ValueSwappable and RandomAccessIterator.
- */
-
- template <typename Point_container,
- typename OutputIterator>
- void pick_n_random_points(Point_container const &points,
- unsigned final_size,
+
+/**
+ * \ingroup subsampling
+ * \brief Subsample a point set by picking random vertices.
+ *
+ * \details It chooses `final_size` distinct points from a random access range `points`
+ * and outputs them to the output iterator `output_it`.
+ * Point_container::iterator should be ValueSwappable and RandomAccessIterator.
+ */
+template <typename Point_container,
+typename OutputIterator>
+void pick_n_random_points(Point_container const &points,
+ std::size_t final_size,
OutputIterator output_it) {
#ifdef GUDHI_SUBS_PROFILING
- Gudhi::Clock t;
+ Gudhi::Clock t;
#endif
- unsigned nbP = boost::size(points);
- assert(nbP >= final_size);
- std::vector<int> landmarks(nbP);
- std::iota(landmarks.begin(), landmarks.end(), 0);
+ std::size_t nbP = boost::size(points);
+ assert(nbP >= final_size);
+ std::vector<int> landmarks(nbP);
+ std::iota(landmarks.begin(), landmarks.end(), 0);
+
+ std::random_device rd;
+ std::mt19937 g(rd());
- std::random_device rd;
- std::mt19937 g(rd());
-
- std::shuffle(landmarks.begin(), landmarks.end(), g);
- landmarks.resize(final_size);
+ std::shuffle(landmarks.begin(), landmarks.end(), g);
+ landmarks.resize(final_size);
+
+ for (int l : landmarks)
+ *output_it++ = points[l];
- for (int l: landmarks)
- *output_it++ = points[l];
-
#ifdef GUDHI_SUBS_PROFILING
- t.end();
- std::cerr << "Random landmark choice took " << t.num_seconds()
+ t.end();
+ std::cerr << "Random landmark choice took " << t.num_seconds()
<< " seconds." << std::endl;
#endif
- }
+}
+
+} // namespace subsampling
-} // namesapce subsampling
-
} // namespace Gudhi
-#endif // PICK_RANDOM_POINTS_H_
+#endif // PICK_N_RANDOM_POINTS_H_
diff --git a/src/Subsampling/include/gudhi/sparsify_point_set.h b/src/Subsampling/include/gudhi/sparsify_point_set.h
index 607555b4..7ff11b4c 100644
--- a/src/Subsampling/include/gudhi/sparsify_point_set.h
+++ b/src/Subsampling/include/gudhi/sparsify_point_set.h
@@ -20,8 +20,8 @@
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
-#ifndef GUDHI_SPARSIFY_POINT_SET_H
-#define GUDHI_SPARSIFY_POINT_SET_H
+#ifndef SPARSIFY_POINT_SET_H_
+#define SPARSIFY_POINT_SET_H_
#include <gudhi/Kd_tree_search.h>
#ifdef GUDHI_SUBSAMPLING_PROFILING
@@ -32,6 +32,7 @@
#include <vector>
namespace Gudhi {
+
namespace subsampling {
/**
@@ -54,16 +55,14 @@ namespace subsampling {
* @param[in] min_squared_dist Minimum squared distance separating the output points.
* @param[out] output_it The output iterator.
*/
-
template <typename Kernel, typename Point_range, typename OutputIterator>
void
sparsify_point_set(
- const Kernel &k, Point_range const& input_pts,
- typename Kernel::FT min_squared_dist,
- OutputIterator output_it)
-{
+ const Kernel &k, Point_range const& input_pts,
+ typename Kernel::FT min_squared_dist,
+ OutputIterator output_it) {
typedef typename Gudhi::spatial_searching::Kd_tree_search<
- Kernel, Point_range> Points_ds;
+ Kernel, Point_range> Points_ds;
typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
@@ -78,10 +77,9 @@ sparsify_point_set(
// Parse the input points, and add them if they are not too close to
// the other points
std::size_t pt_idx = 0;
- for (typename Point_range::const_iterator it_pt = input_pts.begin() ;
- it_pt != input_pts.end();
- ++it_pt, ++pt_idx)
- {
+ for (typename Point_range::const_iterator it_pt = input_pts.begin();
+ it_pt != input_pts.end();
+ ++it_pt, ++pt_idx) {
if (dropped_points[pt_idx])
continue;
@@ -90,30 +88,28 @@ sparsify_point_set(
auto ins_range = points_ds.query_incremental_nearest_neighbors(*it_pt);
// If another point Q is closer that min_squared_dist, mark Q to be dropped
- for (auto const& neighbor : ins_range)
- {
+ for (auto const& neighbor : ins_range) {
std::size_t neighbor_point_idx = neighbor.first;
// If the neighbor is too close, we drop the neighbor
- if (neighbor.second < min_squared_dist)
- {
- // N.B.: If neighbor_point_idx < pt_idx,
+ if (neighbor.second < min_squared_dist) {
+ // N.B.: If neighbor_point_idx < pt_idx,
// dropped_points[neighbor_point_idx] is already true but adding a
// test doesn't make things faster, so why bother?
dropped_points[neighbor_point_idx] = true;
- }
- else
+ } else {
break;
+ }
}
}
#ifdef GUDHI_SUBSAMPLING_PROFILING
t.end();
std::cerr << "Point set sparsified in " << t.num_seconds()
- << " seconds." << std::endl;
+ << " seconds." << std::endl;
#endif
}
-} // namespace subsampling
-} // namespace Gudhi
+} // namespace subsampling
+} // namespace Gudhi
-#endif // GUDHI_POINT_CLOUD_H
+#endif // SPARSIFY_POINT_SET_H_
diff --git a/src/Subsampling/test/CMakeLists.txt b/src/Subsampling/test/CMakeLists.txt
index 91d4ed8f..3a2fb649 100644
--- a/src/Subsampling/test/CMakeLists.txt
+++ b/src/Subsampling/test/CMakeLists.txt
@@ -12,8 +12,6 @@ endif()
if(CGAL_FOUND)
if (NOT CGAL_VERSION VERSION_LESS 4.8.1)
- message(STATUS "CGAL version: ${CGAL_VERSION}.")
-
if (EIGEN3_FOUND)
add_executable( Subsampling_test_pick_n_random_points test_pick_n_random_points.cpp )
target_link_libraries(Subsampling_test_pick_n_random_points ${CGAL_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
@@ -23,12 +21,18 @@ if(CGAL_FOUND)
add_executable(Subsampling_test_sparsify_point_set test_sparsify_point_set.cpp)
target_link_libraries(Subsampling_test_sparsify_point_set ${CGAL_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
- else()
- message(WARNING "Eigen3 not found. Version 3.1.0 is required for Subsampling feature.")
+
+ add_test(Subsampling_test_pick_n_random_points ${CMAKE_CURRENT_BINARY_DIR}/Subsampling_test_pick_n_random_points
+ # XML format for Jenkins xUnit plugin
+ --log_format=XML --log_sink=${CMAKE_SOURCE_DIR}/Subsampling_test_pick_n_random_points_UT.xml --log_level=test_suite --report_level=no)
+
+ add_test(Subsampling_test_choose_n_farthest_points ${CMAKE_CURRENT_BINARY_DIR}/Subsampling_test_choose_n_farthest_points
+ # XML format for Jenkins xUnit plugin
+ --log_format=XML --log_sink=${CMAKE_SOURCE_DIR}/Subsampling_test_choose_n_farthest_points_UT.xml --log_level=test_suite --report_level=no)
+
+ add_test(Subsampling_test_sparsify_point_set ${CMAKE_CURRENT_BINARY_DIR}/Subsampling_test_sparsify_point_set
+ # XML format for Jenkins xUnit plugin
+ --log_format=XML --log_sink=${CMAKE_SOURCE_DIR}/Subsampling_test_sparsify_point_set_UT.xml --log_level=test_suite --report_level=no)
endif()
- else()
- message(WARNING "CGAL version: ${CGAL_VERSION} is too old to compile Subsampling tests. Version 4.8.1 is required.")
endif ()
-else()
- message(WARNING "CGAL not found. It is required for the Subsampling tests.")
endif()
diff --git a/src/Subsampling/test/test_choose_n_farthest_points.cpp b/src/Subsampling/test/test_choose_n_farthest_points.cpp
index f79a4dfb..d064899a 100644
--- a/src/Subsampling/test/test_choose_n_farthest_points.cpp
+++ b/src/Subsampling/test/test_choose_n_farthest_points.cpp
@@ -35,10 +35,9 @@
#include <CGAL/Epick_d.h>
-typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K;
-typedef typename K::FT FT;
-typedef typename K::Point_d Point_d;
-
+typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K;
+typedef typename K::FT FT;
+typedef typename K::Point_d Point_d;
BOOST_AUTO_TEST_CASE(test_choose_farthest_point) {
std::vector< Point_d > points, landmarks;
@@ -52,6 +51,6 @@ BOOST_AUTO_TEST_CASE(test_choose_farthest_point) {
landmarks.clear();
K k;
Gudhi::subsampling::choose_n_farthest_points(k, points, 100, std::back_inserter(landmarks));
-
+
BOOST_CHECK(landmarks.size() == 100);
}
diff --git a/src/Subsampling/test/test_sparsify_point_set.cpp b/src/Subsampling/test/test_sparsify_point_set.cpp
index 61f6fa18..f993d6d6 100644
--- a/src/Subsampling/test/test_sparsify_point_set.cpp
+++ b/src/Subsampling/test/test_sparsify_point_set.cpp
@@ -29,21 +29,19 @@
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
-#include <array>
#include <vector>
#include <iterator>
BOOST_AUTO_TEST_CASE(test_sparsify_point_set)
{
typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
- typedef typename K::FT FT;
typedef typename K::Point_d Point_d;
CGAL::Random rd;
std::vector<Point_d> points;
for (int i = 0 ; i < 500 ; ++i)
- points.push_back(Point_d(std::array<FT,4>({rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1)})));
+ points.push_back(Point_d(rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1),rd.get_double(-1.,1)));
K k;
std::vector<Point_d> results;
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-03 14:26:42 +0000