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-rw-r--r--src/Subsampling/test/landmarking.cpp8
-rw-r--r--src/Witness_complex/example/witness_complex_from_file.cpp4
-rw-r--r--src/Witness_complex/example/witness_complex_sphere.cpp4
-rw-r--r--src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h105
-rw-r--r--src/Witness_complex/include/gudhi/Landmark_choice_by_random_point.h96
-rw-r--r--src/Witness_complex/test/witness_complex_points.cpp5
6 files changed, 10 insertions, 212 deletions
diff --git a/src/Subsampling/test/landmarking.cpp b/src/Subsampling/test/landmarking.cpp
index 3131c798..a2c85349 100644
--- a/src/Subsampling/test/landmarking.cpp
+++ b/src/Subsampling/test/landmarking.cpp
@@ -2,8 +2,8 @@
// # define TBB_USE_THREADING_TOOL
// #endif
-#include <gudhi/Landmark_choice_by_random_point.h>
-#include <gudhi/Landmark_choice_by_farthest_point.h>
+#include <gudhi/pick_random_points.h>
+#include <gudhi/choose_by_farthest_point.h>
#include <vector>
#include <iterator>
@@ -35,13 +35,13 @@ int main() {
std::vector<Point_d> landmarks;
- Gudhi::landmark_choice_by_random_point(vect, 5, std::back_inserter(landmarks));
+ Gudhi::pick_random_points(vect, 5, std::back_inserter(landmarks));
std::cout << "landmark vector contains: ";
for (auto l: landmarks)
std::cout << l << "\n";
landmarks.clear();
K k;
- Gudhi::landmark_choice_by_farthest_point(k, vect, 16, std::back_inserter(landmarks));
+ Gudhi::choose_by_farthest_point(k, vect, 16, std::back_inserter(landmarks));
}
diff --git a/src/Witness_complex/example/witness_complex_from_file.cpp b/src/Witness_complex/example/witness_complex_from_file.cpp
index fbc3cf1d..17b63dcf 100644
--- a/src/Witness_complex/example/witness_complex_from_file.cpp
+++ b/src/Witness_complex/example/witness_complex_from_file.cpp
@@ -26,7 +26,7 @@
#include <gudhi/Simplex_tree.h>
#include <gudhi/Witness_complex.h>
#include <gudhi/Construct_closest_landmark_table.h>
-#include <gudhi/Landmark_choice_by_random_point.h>
+#include <gudhi/pick_random_points.h>
#include <gudhi/reader_utils.h>
#include <iostream>
@@ -87,7 +87,7 @@ int main(int argc, char * const argv[]) {
// Choose landmarks
start = clock();
std::vector<std::vector< int > > knn;
- Gudhi::landmark_choice_by_random_point(point_vector, 100, std::back_inserter(landmarks));
+ Gudhi::pick_random_points(point_vector, 100, std::back_inserter(landmarks));
Gudhi::witness_complex::construct_closest_landmark_table(point_vector, landmarks, knn);
end = clock();
std::cout << "Landmark choice for " << nbL << " landmarks took "
diff --git a/src/Witness_complex/example/witness_complex_sphere.cpp b/src/Witness_complex/example/witness_complex_sphere.cpp
index 9cf2f119..495a5895 100644
--- a/src/Witness_complex/example/witness_complex_sphere.cpp
+++ b/src/Witness_complex/example/witness_complex_sphere.cpp
@@ -28,7 +28,7 @@
#include <gudhi/Simplex_tree.h>
#include <gudhi/Witness_complex.h>
#include <gudhi/Construct_closest_landmark_table.h>
-#include <gudhi/Landmark_choice_by_random_point.h>
+#include <gudhi/pick_random_points.h>
#include <gudhi/reader_utils.h>
#include <iostream>
@@ -76,7 +76,7 @@ int main(int argc, char * const argv[]) {
// Choose landmarks
start = clock();
std::vector<std::vector< int > > knn;
- Gudhi::landmark_choice_by_random_point(point_vector, 100, std::back_inserter(landmarks));
+ Gudhi::pick_random_points(point_vector, 100, std::back_inserter(landmarks));
Gudhi::witness_complex::construct_closest_landmark_table(point_vector, landmarks, knn);
// Compute witness complex
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
deleted file mode 100644
index df93155b..00000000
--- a/src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/* 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_FURTHEST_POINT_H_
-#define LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
-
-#include <boost/range/size.hpp>
-
-#include <limits> // for numeric_limits<>
-#include <iterator>
-#include <algorithm> // for sort
-#include <vector>
-
-namespace Gudhi {
-
-namespace witness_complex {
-
- typedef std::vector<int> typeVectorVertex;
-
- /**
- * \ingroup witness_complex
- * \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`.
- *
- * 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 KNearestNeighbours,
- typename Point_random_access_range>
- void landmark_choice_by_furthest_point(Point_random_access_range const &points,
- int nbL,
- KNearestNeighbours &knn) {
- int nb_points = boost::size(points);
- 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
- // or better yet std::uniform_int_distribution
- 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, *(std::begin(points) + 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 (int i = 0; i < nb_points; ++i)
- std::sort(std::begin(knn[i]),
- std::end(knn[i]),
- [&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 // LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
diff --git a/src/Witness_complex/include/gudhi/Landmark_choice_by_random_point.h b/src/Witness_complex/include/gudhi/Landmark_choice_by_random_point.h
deleted file mode 100644
index ebf6aad1..00000000
--- a/src/Witness_complex/include/gudhi/Landmark_choice_by_random_point.h
+++ /dev/null
@@ -1,96 +0,0 @@
-/* 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_RANDOM_POINT_H_
-#define LANDMARK_CHOICE_BY_RANDOM_POINT_H_
-
-#include <boost/range/size.hpp>
-
-#include <queue> // for priority_queue<>
-#include <utility> // for pair<>
-#include <iterator>
-#include <vector>
-#include <set>
-
-namespace Gudhi {
-
-namespace witness_complex {
-
- /**
- * \ingroup witness_complex
- * \brief Landmark choice strategy by taking random vertices for landmarks.
- * \details It chooses nbL distinct landmarks from a random access range `points`
- * and outputs a matrix {witness}*{closest landmarks} 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 and
- * Vertex_handle is the label type of a vertex in a simplicial complex.
- * Closest_landmark_range needs to have push_back operation.
- */
-
- template <typename KNearestNeighbours,
- typename Point_random_access_range>
- void landmark_choice_by_random_point(Point_random_access_range const &points,
- int nbL,
- KNearestNeighbours &knn) {
- int nbP = boost::size(points);
- assert(nbP >= nbL);
- std::set<int> landmarks;
- int current_number_of_landmarks = 0; // counter for landmarks
-
- // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety
- int chosen_landmark = rand() % nbP;
- for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) {
- while (landmarks.find(chosen_landmark) != landmarks.end())
- chosen_landmark = rand() % nbP;
- landmarks.insert(chosen_landmark);
- }
-
- int dim = boost::size(*std::begin(points));
- typedef std::pair<double, int> dist_i;
- typedef bool (*comp)(dist_i, dist_i);
- knn = KNearestNeighbours(nbP);
- for (int points_i = 0; points_i < nbP; points_i++) {
- std::priority_queue<dist_i, std::vector<dist_i>, comp> l_heap([](dist_i j1, dist_i j2) {
- return j1.first > j2.first;
- });
- std::set<int>::iterator landmarks_it;
- int landmarks_i = 0;
- for (landmarks_it = landmarks.begin(), landmarks_i = 0; landmarks_it != landmarks.end();
- ++landmarks_it, landmarks_i++) {
- dist_i dist = std::make_pair(euclidean_distance(points[points_i], points[*landmarks_it]), landmarks_i);
- l_heap.push(dist);
- }
- for (int i = 0; i < dim + 1; i++) {
- dist_i dist = l_heap.top();
- knn[points_i].push_back(dist.second);
- l_heap.pop();
- }
- }
- }
-
-} // namespace witness_complex
-
-} // namespace Gudhi
-
-#endif // LANDMARK_CHOICE_BY_RANDOM_POINT_H_
diff --git a/src/Witness_complex/test/witness_complex_points.cpp b/src/Witness_complex/test/witness_complex_points.cpp
index c0006142..596152f4 100644
--- a/src/Witness_complex/test/witness_complex_points.cpp
+++ b/src/Witness_complex/test/witness_complex_points.cpp
@@ -28,8 +28,7 @@
#include <gudhi/Simplex_tree.h>
#include <gudhi/Witness_complex.h>
#include <gudhi/Construct_closest_landmark_table.h>
-#include <gudhi/Landmark_choice_by_random_point.h>
-#include <gudhi/Landmark_choice_by_farthest_point.h>
+#include <gudhi/pick_random_points.h>
#include <iostream>
#include <vector>
@@ -51,7 +50,7 @@ BOOST_AUTO_TEST_CASE(witness_complex_points) {
bool b_print_output = false;
// First test: random choice
Simplex_tree complex1;
- Gudhi::landmark_choice_by_random_point(points, 100, std::back_inserter(landmarks));
+ Gudhi::pick_random_points(points, 100, std::back_inserter(landmarks));
Gudhi::witness_complex::construct_closest_landmark_table(points, landmarks, knn);
assert(!knn.empty());
WitnessComplex witnessComplex1(knn, 100, 3, complex1);