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author | cjamin <cjamin@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2016-05-31 16:09:46 +0000 |
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committer | cjamin <cjamin@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2016-05-31 16:09:46 +0000 |
commit | bb56631552ff8cf431d2286470223f7394cb2846 (patch) | |
tree | bf45bcc0cc2d4813fb5c59f6e0c44fb7a72e267a /src/Subsampling/include/gudhi/Landmark_choice_by_farthest_point.h | |
parent | 29cf10daf5e6f2674ccb1491716754a4e5f98cc2 (diff) |
Farthest points (from Siargey)
git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1230 636b058d-ea47-450e-bf9e-a15bfbe3eedb
Former-commit-id: 3093bfd41948fecf5f6ccb2d1d77546c12e628f4
Diffstat (limited to 'src/Subsampling/include/gudhi/Landmark_choice_by_farthest_point.h')
-rw-r--r-- | src/Subsampling/include/gudhi/Landmark_choice_by_farthest_point.h | 158 |
1 files changed, 158 insertions, 0 deletions
diff --git a/src/Subsampling/include/gudhi/Landmark_choice_by_farthest_point.h b/src/Subsampling/include/gudhi/Landmark_choice_by_farthest_point.h new file mode 100644 index 00000000..198c9f9f --- /dev/null +++ b/src/Subsampling/include/gudhi/Landmark_choice_by_farthest_point.h @@ -0,0 +1,158 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Siargey Kachanovich + * + * Copyright (C) 2015 INRIA Sophia Antipolis-Méditerranée (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see <http://www.gnu.org/licenses/>. + */ + +#ifndef LANDMARK_CHOICE_BY_FARTHEST_POINT_H_ +#define LANDMARK_CHOICE_BY_FARTHEST_POINT_H_ + +#include <gudhi/Spatial_tree_data_structure.h> + +#include <iterator> +#include <algorithm> // for sort +#include <vector> +#include <random> +#include <boost/heap/fibonacci_heap.hpp> + +namespace Gudhi { + + + template < typename Point_d, + typename Heap, + typename Tree, + typename Presence_table > + void update_heap( Point_d &l, + unsigned nbL, + Heap &heap, + Tree &tree, + Presence_table &table) + { + auto search = tree.query_incremental_ANN(l); + for (auto w: search) { + if (table[w.first].first) + if (w.second < table[w.first].second->second) { + heap.update(table[w.first].second, w); + } + } + } + + /** + * \ingroup witness_complex + * \brief Landmark choice strategy by iteratively adding the farthest witness from the + * current landmark set as the new landmark. + * \details It chooses nbL landmarks from a random access range `points` and + * writes {witness}*{closest landmarks} matrix in `knn`. + * + * The type KNearestNeighbors can be seen as + * Witness_range<Closest_landmark_range<Vertex_handle>>, where + * Witness_range and Closest_landmark_range are random access ranges + * + */ + + template < typename Kernel, + typename Point_container, + typename OutputIterator> + void landmark_choice_by_farthest_point( Kernel& k, + Point_container const &points, + int nbL, + OutputIterator output_it) + { + + // typedef typename Kernel::FT FT; + // typedef std::pair<unsigned, FT> Heap_node; + + // struct R_max_compare + // { + // bool operator()(const Heap_node &rmh1, const Heap_node &rmh2) const + // { + // return rmh1.second < rmh2.second; + // } + // }; + + // typedef boost::heap::fibonacci_heap<Heap_node, boost::heap::compare<R_max_compare>> Heap; + // typedef Spatial_tree_data_structure<Kernel, Point_container> Tree; + // typedef std::vector< std::pair<bool, Heap_node*> > Presence_table; + + typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object(); + + // Tree tree(points); + // Heap heap; + // Presence_table table(points.size()); + // for (auto p: table) + // std::cout << p.first << "\n"; + // int number_landmarks = 0; // number of treated landmarks + + // 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(points.size(), infty); // vector of current distances to L from points + + // // Choose randomly the first landmark + // std::random_device rd; + // std::mt19937 gen(rd()); + // std::uniform_int_distribution<> dis(1, 6); + // int curr_landmark = dis(gen); + + // do { + // *output_landmarks++ = points[curr_landmark]; + // std::cout << curr_landmark << "\n"; + // number_landmarks++; + // } + // while (number_landmarks < nbL); + // } + + int nb_points = boost::size(points); + assert(nb_points >= nbL); + + int current_number_of_landmarks = 0; // counter for landmarks + 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 points + + // Choose randomly the first landmark + std::random_device rd; + std::mt19937 gen(rd()); + std::uniform_int_distribution<> dis(1, 6); + int curr_max_w = dis(gen); + + + for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) { + // curr_max_w at this point is the next landmark + *output_it++ = points[curr_max_w]; + std::cout << curr_max_w << "\n"; + unsigned i = 0; + for (auto& p : points) { + double curr_dist = sqdist(p, *(std::begin(points) + curr_max_w)); + if (curr_dist < dist_to_L[i]) + dist_to_L[i] = curr_dist; + ++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; + } + } + } + +} // namespace Gudhi + +#endif // LANDMARK_CHOICE_BY_FARTHEST_POINT_H_ |