-- 
cgit v1.2.3

From 6f9560fe6d6cca6c5a0de35fcf7938912e103930 Mon Sep 17 00:00:00 2001
From: glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Fri, 24 Feb 2017 14:37:59 +0000
Subject: Drop unused include gudhi/Clock.h.

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/farthest_distance@2108 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: a210a519993cb4f0118fb250e76afea10d20bad3
---
 src/Subsampling/include/gudhi/choose_n_farthest_points.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index a77d0cd7..5061a511 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -26,7 +26,6 @@
 #include <boost/range.hpp>
 
 #include <gudhi/Null_output_iterator.h>
-#include <gudhi/Clock.h>
 
 #include <iterator>
 #include <vector>
-- 
cgit v1.2.3


From d2e5de256424e824e9f54a6ae6c30cd06d8111d4 Mon Sep 17 00:00:00 2001
From: glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Sun, 26 Mar 2017 12:38:26 +0000
Subject: Remove overload of choose_n_farthest_points. Use a named constant
 instead of -1 to denote a random value.

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/farthest_distance@2245 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 166dff85c74ab2c13bfd788f226d1d8a15d1d1b9
---
 .../example/example_choose_n_farthest_points.cpp   |  2 +-
 src/Subsampling/example/example_custom_kernel.cpp  |  2 +-
 .../include/gudhi/choose_n_farthest_points.h       | 45 ++++++----------------
 src/cython/include/Subsampling_interface.h         |  2 +-
 4 files changed, 15 insertions(+), 36 deletions(-)

diff --git a/src/Subsampling/example/example_choose_n_farthest_points.cpp b/src/Subsampling/example/example_choose_n_farthest_points.cpp
index 533aba74..adbd2b80 100644
--- a/src/Subsampling/example/example_choose_n_farthest_points.cpp
+++ b/src/Subsampling/example/example_choose_n_farthest_points.cpp
@@ -19,7 +19,7 @@ int main(void) {
 
   K k;
   std::vector<Point_d> results;
-  Gudhi::subsampling::choose_n_farthest_points(k, points, 100, std::back_inserter(results));
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 100, Gudhi::subsampling::random_first_landmark, std::back_inserter(results));
   std::cout << "Before sparsification: " << points.size() << " points.\n";
   std::cout << "After  sparsification: " << results.size() << " points.\n";
 
diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp
index 25b5bf6c..935ba885 100644
--- a/src/Subsampling/example/example_custom_kernel.cpp
+++ b/src/Subsampling/example/example_custom_kernel.cpp
@@ -54,7 +54,7 @@ int main(void) {
   std::vector<Point_d> points = {0, 1, 2, 3};
   std::vector<Point_d> results;
 
-  Gudhi::subsampling::choose_n_farthest_points(k, points, 2, std::back_inserter(results));
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 2, Gudhi::subsampling::random_first_landmark, std::back_inserter(results));
   std::cout << "Before sparsification: " << points.size() << " points.\n";
   std::cout << "After  sparsification: " << results.size() << " points.\n";
   std::cout << "Result table: {" << results[0] << "," << results[1] << "}\n";
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index 5061a511..02376c03 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -36,11 +36,21 @@ namespace Gudhi {
 
 namespace subsampling {
 
+/**
+ *  \ingroup subsampling
+ */
+enum : std::size_t {
+/**
+ *  Argument for `choose_n_farthest_points` to indicate that the starting point should be picked randomly.
+ */
+  random_first_landmark = std::size_t(-1)
+};
+
 /** 
  *  \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` or, if `starting point==-1`, with a random landmark.
+ *  The iteration starts with the landmark `starting point` or, if `starting point==random_first_landmark`, with a random landmark.
  *  \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the 
  *          concept <a target="_blank"
  *   href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a>
@@ -80,7 +90,7 @@ void choose_n_farthest_points(Kernel const &k,
   if (final_size < 1)
     return;
 
-  if (starting_point == std::size_t(-1)) {
+  if (starting_point == random_first_landmark) {
     // Choose randomly the first landmark
     std::random_device rd;
     std::mt19937 gen(rd());
@@ -117,37 +127,6 @@ void choose_n_farthest_points(Kernel const &k,
   }
 }
 
-/** 
- *  \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.
- *  \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the 
- *          concept <a target="_blank"
- *   href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a>
- *   concept.
- *  It must also contain a public member 'squared_distance_d_object' of this type.
- *  \tparam Point_range Range whose value type is Kernel::Point_d.  It must provide random-access 
- *         via `operator[]` and the points should be stored contiguously in memory.
- *  \tparam PointOutputIterator Output iterator whose value type is Kernel::Point_d.
- *  \details It chooses `final_size` points from a random access range `input_pts` and
- *  outputs it in the output iterator `output_it`.
- * @param[in] k A kernel object.
- * @param[in] input_pts Const reference to the input points.
- * @param[in] final_size The size of the subsample to compute.
- * @param[out] output_it The output iterator.
- *  
- */
-template < typename Kernel,
-typename Point_range,
-typename PointOutputIterator>
-void choose_n_farthest_points(Kernel const& k,
-                              Point_range const &input_pts,
-                              std::size_t final_size,
-                              PointOutputIterator output_it) {
-  choose_n_farthest_points(k, input_pts, final_size, -1, output_it);
-}
-
 }  // namespace subsampling
 
 }  // namespace Gudhi
diff --git a/src/cython/include/Subsampling_interface.h b/src/cython/include/Subsampling_interface.h
index 1c6032c0..a4a872e4 100644
--- a/src/cython/include/Subsampling_interface.h
+++ b/src/cython/include/Subsampling_interface.h
@@ -46,7 +46,7 @@ std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector
                                                                unsigned nb_points) {
   std::vector<std::vector<double>> landmarks;
   Subsampling_dynamic_kernel k;
-  choose_n_farthest_points(k, points, nb_points, std::back_inserter(landmarks));
+  choose_n_farthest_points(k, points, nb_points, random_first_landmark, std::back_inserter(landmarks));
 
   return landmarks;
 }
-- 
cgit v1.2.3


From 732eb3a08d9a16e8e8ffd66ed76b99968d5cb06f Mon Sep 17 00:00:00 2001
From: glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Mon, 27 Mar 2017 15:49:37 +0000
Subject: Rename random_first_landmark to random_starting_point.

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/farthest_distance@2250 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 953403f208094ca4145d837f736bfc939efa9223
---
 src/Subsampling/example/example_choose_n_farthest_points.cpp | 2 +-
 src/Subsampling/example/example_custom_kernel.cpp            | 2 +-
 src/Subsampling/include/gudhi/choose_n_farthest_points.h     | 6 +++---
 src/cython/include/Subsampling_interface.h                   | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/Subsampling/example/example_choose_n_farthest_points.cpp b/src/Subsampling/example/example_choose_n_farthest_points.cpp
index adbd2b80..fc9ea7a6 100644
--- a/src/Subsampling/example/example_choose_n_farthest_points.cpp
+++ b/src/Subsampling/example/example_choose_n_farthest_points.cpp
@@ -19,7 +19,7 @@ int main(void) {
 
   K k;
   std::vector<Point_d> results;
-  Gudhi::subsampling::choose_n_farthest_points(k, points, 100, Gudhi::subsampling::random_first_landmark, std::back_inserter(results));
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 100, Gudhi::subsampling::random_starting_point, std::back_inserter(results));
   std::cout << "Before sparsification: " << points.size() << " points.\n";
   std::cout << "After  sparsification: " << results.size() << " points.\n";
 
diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp
index 935ba885..2719ee90 100644
--- a/src/Subsampling/example/example_custom_kernel.cpp
+++ b/src/Subsampling/example/example_custom_kernel.cpp
@@ -54,7 +54,7 @@ int main(void) {
   std::vector<Point_d> points = {0, 1, 2, 3};
   std::vector<Point_d> results;
 
-  Gudhi::subsampling::choose_n_farthest_points(k, points, 2, Gudhi::subsampling::random_first_landmark, std::back_inserter(results));
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 2, Gudhi::subsampling::random_starting_point, std::back_inserter(results));
   std::cout << "Before sparsification: " << points.size() << " points.\n";
   std::cout << "After  sparsification: " << results.size() << " points.\n";
   std::cout << "Result table: {" << results[0] << "," << results[1] << "}\n";
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index 02376c03..1d2338cb 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -43,14 +43,14 @@ enum : std::size_t {
 /**
  *  Argument for `choose_n_farthest_points` to indicate that the starting point should be picked randomly.
  */
-  random_first_landmark = std::size_t(-1)
+  random_starting_point = std::size_t(-1)
 };
 
 /** 
  *  \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` or, if `starting point==random_first_landmark`, with a random landmark.
+ *  The iteration starts with the landmark `starting point` or, if `starting point==random_starting_point`, with a random landmark.
  *  \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the 
  *          concept <a target="_blank"
  *   href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a>
@@ -90,7 +90,7 @@ void choose_n_farthest_points(Kernel const &k,
   if (final_size < 1)
     return;
 
-  if (starting_point == random_first_landmark) {
+  if (starting_point == random_starting_point) {
     // Choose randomly the first landmark
     std::random_device rd;
     std::mt19937 gen(rd());
diff --git a/src/cython/include/Subsampling_interface.h b/src/cython/include/Subsampling_interface.h
index a4a872e4..b0f4a50a 100644
--- a/src/cython/include/Subsampling_interface.h
+++ b/src/cython/include/Subsampling_interface.h
@@ -46,7 +46,7 @@ std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector
                                                                unsigned nb_points) {
   std::vector<std::vector<double>> landmarks;
   Subsampling_dynamic_kernel k;
-  choose_n_farthest_points(k, points, nb_points, random_first_landmark, std::back_inserter(landmarks));
+  choose_n_farthest_points(k, points, nb_points, random_starting_point, std::back_inserter(landmarks));
 
   return landmarks;
 }
-- 
cgit v1.2.3


From 99d4649ada3b6314269bb22d2a8d95b4011a15ec Mon Sep 17 00:00:00 2001
From: glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Fri, 7 Apr 2017 08:02:39 +0000
Subject: Clarify doc about Squared_distance_d in Subsampling.

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/farthest_distance@2315 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 5559e1c426569fba1dd4cd350a8697ab7e3260f1
---
 src/Subsampling/include/gudhi/choose_n_farthest_points.h | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index 1d2338cb..50d3cf80 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -53,9 +53,8 @@ enum : std::size_t {
  *  The iteration starts with the landmark `starting point` or, if `starting point==random_starting_point`, with a random landmark.
  *  \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the 
  *          concept <a target="_blank"
- *   href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a>
- *   concept.
- *  It must also contain a public member 'squared_distance_d_object' of this type.
+ *   href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a> (despite the name, taken from CGAL, this can be any kind of metric or proximity measure).
+ *  It must also contain a public member `squared_distance_d_object()` that returns an object of this type.
  *  \tparam Point_range Range whose value type is Kernel::Point_d.  It must provide random-access 
  *         via `operator[]` and the points should be stored contiguously in memory.
  *  \tparam PointOutputIterator Output iterator whose value type is Kernel::Point_d.
-- 
cgit v1.2.3


From c536d49278965cd578281ecbec72cea2c8b90cc3 Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Mon, 10 Apr 2017 16:23:30 +0000
Subject: Fix and rename test farthest

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/farthest_distance@2328 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: f1761bf66bcf6d45f72a051fa9a62c4699e3999d
---
 src/Subsampling/test/test_choose_n_farthest_points.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/Subsampling/test/test_choose_n_farthest_points.cpp b/src/Subsampling/test/test_choose_n_farthest_points.cpp
index 6bc5f7b0..ee9d4c77 100644
--- a/src/Subsampling/test/test_choose_n_farthest_points.cpp
+++ b/src/Subsampling/test/test_choose_n_farthest_points.cpp
@@ -25,7 +25,7 @@
 // #endif
 
 #define BOOST_TEST_DYN_LINK
-#define BOOST_TEST_MODULE "witness_complex_points"
+#define BOOST_TEST_MODULE Subsampling - test choose_n_farthest_points
 #include <boost/test/unit_test.hpp>
 #include <boost/mpl/list.hpp>
 
@@ -56,7 +56,7 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point, Kernel, list_of_tested
 
   landmarks.clear();
   Kernel k;
-  Gudhi::subsampling::choose_n_farthest_points(k, points, 100, std::back_inserter(landmarks));
+  Gudhi::subsampling::choose_n_farthest_points(k, points, 100, Gudhi::subsampling::random_starting_point, std::back_inserter(landmarks));
 
   BOOST_CHECK(landmarks.size() == 100);
   for (auto landmark : landmarks)
-- 
cgit v1.2.3