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authorskachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb>2016-12-14 18:08:09 +0000
committerskachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb>2016-12-14 18:08:09 +0000
commit04f4501b35eaa2bd33393ef2445d038251ba1355 (patch)
treeb85b6cca1b28873c9289c1c904be5c1d5d5c0aa7 /src/Subsampling/example/example_custom_kernel.cpp
parent9e8db290ff0b3f69f88fa5ed54482bfb6730ad9b (diff)
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
Diffstat (limited to 'src/Subsampling/example/example_custom_kernel.cpp')
-rw-r--r--src/Subsampling/example/example_custom_kernel.cpp69
1 files changed, 69 insertions, 0 deletions
diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp
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+++ b/src/Subsampling/example/example_custom_kernel.cpp
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+#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;
+}