From 04f4501b35eaa2bd33393ef2445d038251ba1355 Mon Sep 17 00:00:00 2001
From: skachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Wed, 14 Dec 2016 18:08:09 +0000
Subject: Added an example with a distance matrix for the farthest point
 algorithm

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1874 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 340e465189dc7ec8f8706e60e2d8097b53bfd5a0
---
 src/Subsampling/example/CMakeLists.txt             |  1 +
 src/Subsampling/example/example_custom_kernel.cpp  | 69 ++++++++++++++++++++++
 .../include/gudhi/choose_n_farthest_points.h       |  4 +-
 3 files changed, 73 insertions(+), 1 deletion(-)
 create mode 100644 src/Subsampling/example/example_custom_kernel.cpp

(limited to 'src/Subsampling')

diff --git a/src/Subsampling/example/CMakeLists.txt b/src/Subsampling/example/CMakeLists.txt
index 54349f0c..0fd3335c 100644
--- a/src/Subsampling/example/CMakeLists.txt
+++ b/src/Subsampling/example/CMakeLists.txt
@@ -6,6 +6,7 @@ if(CGAL_FOUND)
     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_custom_kernel example_custom_kernel.cpp)
       add_executable(Subsampling_example_sparsify_point_set example_sparsify_point_set.cpp)
       target_link_libraries(Subsampling_example_sparsify_point_set ${CGAL_LIBRARY})
 
diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp
new file mode 100644
index 00000000..05797ebe
--- /dev/null
+++ b/src/Subsampling/example/example_custom_kernel.cpp
@@ -0,0 +1,69 @@
+#include <gudhi/choose_n_farthest_points.h>
+
+#include <CGAL/Epick_d.h>
+#include <CGAL/Random.h>
+
+#include <vector>
+#include <iterator>
+
+
+/* The class Kernel contains a distance function defined on the set of points {0,1,2,3}
+ * and computes a distance according to the matrix:
+ * 0 1 2 4
+ * 1 0 4 2
+ * 2 4 0 1
+ * 4 2 1 0
+ */
+class Kernel {
+public:
+  typedef double FT;
+  typedef unsigned Point_d;
+
+  // Class Squared_distance_d
+  class Squared_distance_d {
+  private:
+    std::vector<std::vector<FT>> matrix_;
+    
+  public:
+
+  Squared_distance_d()
+  {
+    matrix_.push_back(std::vector<FT>({0,1,2,4}));
+    matrix_.push_back(std::vector<FT>({1,0,4,2}));
+    matrix_.push_back(std::vector<FT>({2,4,0,1}));
+    matrix_.push_back(std::vector<FT>({4,2,1,0}));
+  }
+  
+    FT operator()(Point_d p1, Point_d p2)
+    {
+      return matrix_[p1][p2];
+    }
+  };
+
+  // Constructor
+  Kernel()
+  {}
+
+  // Object of type Squared_distance_d
+  Squared_distance_d squared_distance_d_object() const
+  {
+    return Squared_distance_d();
+  }
+  
+};
+
+int main(void) {
+  typedef Kernel K;
+  typedef typename K::Point_d Point_d;
+
+  K k;
+  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));
+  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";
+
+  return 0;
+}
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
index 43bf6402..b6b7ace3 100644
--- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h
+++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h
@@ -52,6 +52,7 @@ namespace subsampling {
  *          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 OutputIterator Output iterator whose value type is Kernel::Point_d.
@@ -112,6 +113,7 @@ void choose_n_farthest_points(Kernel const &k,
  *          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 OutputIterator Output iterator whose value type is Kernel::Point_d.
@@ -133,7 +135,7 @@ void choose_n_farthest_points(Kernel const& k,
   // Choose randomly the first landmark
   std::random_device rd;
   std::mt19937 gen(rd());
-  std::uniform_int_distribution<> dis(1, 6);
+  std::uniform_int_distribution<> dis(0, final_size);
   int starting_point = dis(gen);
   choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
 }
-- 
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