diff options
Diffstat (limited to 'src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h')
| -rw-r--r-- | src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h | 123 |
1 files changed, 61 insertions, 62 deletions
diff --git a/src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h b/src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h index 8dfec99c..bb7e87f5 100644 --- a/src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h +++ b/src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h @@ -23,6 +23,10 @@ #ifndef LANDMARK_CHOICE_BY_FURTHEST_POINT_H_ #define LANDMARK_CHOICE_BY_FURTHEST_POINT_H_ +#include <limits> // for numeric_limits<> +#include <algorithm> // for sort +#include <vector> + namespace Gudhi { namespace witness_complex { @@ -36,73 +40,68 @@ namespace witness_complex { */ class Landmark_choice_by_furthest_point { - -private: + private: typedef std::vector<int> typeVectorVertex; - -public: - -/** - * \brief Landmark choice strategy by iteratively adding the furthest 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`. - */ - template <typename KNearestNeighbours, - typename Point_random_access_range> - Landmark_choice_by_furthest_point(Point_random_access_range const &points, - int nbL, - KNearestNeighbours &knn) - { - int nb_points = points.end() - points.begin(); - assert(nb_points >= nbL); - std::vector<std::vector<double>> wit_land_dist(nb_points, std::vector<double>()); // distance matrix witness x landmarks - typeVectorVertex chosen_landmarks; // landmark list - - knn = KNearestNeighbours(nb_points, std::vector<int>()); - 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 - //int dim = points.begin()->size(); - - int rand_int = rand() % nb_points; - int curr_max_w = rand_int; //For testing purposes a pseudo-random number is used here - - 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 - chosen_landmarks.push_back(curr_max_w); - unsigned i = 0; - for (auto& p: points) - { - double curr_dist = euclidean_distance(p, *(points.begin() + chosen_landmarks[current_number_of_landmarks])); - wit_land_dist[i].push_back(curr_dist); - knn[i].push_back(current_number_of_landmarks); - if (curr_dist < dist_to_L[i]) - dist_to_L[i] = curr_dist; - ++i; - } - 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; - } + public: + /** + * \brief Landmark choice strategy by iteratively adding the furthest 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`. + */ + + template <typename KNearestNeighbours, + typename Point_random_access_range> + Landmark_choice_by_furthest_point(Point_random_access_range const &points, + int nbL, + KNearestNeighbours &knn) { + int nb_points = points.end() - points.begin(); + assert(nb_points >= nbL); + // distance matrix witness x landmarks + std::vector<std::vector<double>> wit_land_dist(nb_points, std::vector<double>()); + // landmark list + typeVectorVertex chosen_landmarks; + + knn = KNearestNeighbours(nb_points, std::vector<int>()); + 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 + + // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety + int rand_int = rand() % nb_points; + int curr_max_w = rand_int; // For testing purposes a pseudo-random number is used here + + 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 + chosen_landmarks.push_back(curr_max_w); + unsigned i = 0; + for (auto& p : points) { + double curr_dist = euclidean_distance(p, *(points.begin() + chosen_landmarks[current_number_of_landmarks])); + wit_land_dist[i].push_back(curr_dist); + knn[i].push_back(current_number_of_landmarks); + if (curr_dist < dist_to_L[i]) + dist_to_L[i] = curr_dist; + ++i; + } + 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; } - for (unsigned i = 0; i < points.size(); ++i) - std::sort(knn[i].begin(), - knn[i].end(), - [&wit_land_dist, i](int a, int b) - { return wit_land_dist[i][a] < wit_land_dist[i][b]; }); } - + for (unsigned i = 0; i < points.size(); ++i) + std::sort(knn[i].begin(), + knn[i].end(), + [&wit_land_dist, i](int a, int b) { + return wit_land_dist[i][a] < wit_land_dist[i][b]; }); + } }; -} +} // namespace witness_complex -} +} // namespace Gudhi -#endif +#endif // LANDMARK_CHOICE_BY_FURTHEST_POINT_H_ |