diff options
Diffstat (limited to 'src/Witness_complex')
8 files changed, 217 insertions, 11 deletions
diff --git a/src/Witness_complex/example/witness_complex_from_file.cpp b/src/Witness_complex/example/witness_complex_from_file.cpp index bb641b3c..59dd28e0 100644 --- a/src/Witness_complex/example/witness_complex_from_file.cpp +++ b/src/Witness_complex/example/witness_complex_from_file.cpp @@ -36,8 +36,9 @@ #include <string> #include <vector> -typedef std::vector< Vertex_handle > typeVectorVertex; +typedef std::vector< int > typeVectorVertex; typedef std::vector< std::vector <double> > Point_Vector; +typedef Gudhi::Simplex_tree<> Simplex_tree; int main(int argc, char * const argv[]) { if (argc != 3) { @@ -51,7 +52,7 @@ int main(int argc, char * const argv[]) { clock_t start, end; // Construct the Simplex Tree - Gudhi::Simplex_tree<> simplex_tree; + Simplex_tree simplex_tree; // Read the OFF file (input file name given as parameter) and triangulate points Gudhi::Points_off_reader<std::vector <double>> off_reader(off_file_name); @@ -69,7 +70,7 @@ int main(int argc, char * const argv[]) { std::vector<std::vector< int > > knn; Point_Vector landmarks; Gudhi::subsampling::pick_n_random_points(point_vector, 100, std::back_inserter(landmarks)); - Gudhi::witness_complex::construct_closest_landmark_table(point_vector, landmarks, knn); + Gudhi::witness_complex::construct_closest_landmark_table<Simplex_tree::Filtration_value>(point_vector, landmarks, knn); end = clock(); std::cout << "Landmark choice for " << nbL << " landmarks took " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n"; diff --git a/src/Witness_complex/example/witness_complex_sphere.cpp b/src/Witness_complex/example/witness_complex_sphere.cpp index e6f88274..7ab86cc0 100644 --- a/src/Witness_complex/example/witness_complex_sphere.cpp +++ b/src/Witness_complex/example/witness_complex_sphere.cpp @@ -40,6 +40,8 @@ #include "generators.h" +typedef Gudhi::Simplex_tree<> Simplex_tree; + /** Write a gnuplot readable file. * Data range is a random access range of pairs (arg, value) */ @@ -62,7 +64,7 @@ int main(int argc, char * const argv[]) { clock_t start, end; // Construct the Simplex Tree - Gudhi::Simplex_tree<> simplex_tree; + Simplex_tree simplex_tree; std::vector< std::pair<int, double> > l_time; @@ -77,7 +79,7 @@ int main(int argc, char * const argv[]) { start = clock(); std::vector<std::vector< int > > knn; Gudhi::subsampling::pick_n_random_points(point_vector, 100, std::back_inserter(landmarks)); - Gudhi::witness_complex::construct_closest_landmark_table(point_vector, landmarks, knn); + Gudhi::witness_complex::construct_closest_landmark_table<Simplex_tree::Filtration_value>(point_vector, landmarks, knn); // Compute witness complex Gudhi::witness_complex::witness_complex(knn, number_of_landmarks, point_vector[0].size(), simplex_tree); diff --git a/src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h b/src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h index ef711c34..ec93ae71 100644 --- a/src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h +++ b/src/Witness_complex/include/gudhi/Construct_closest_landmark_table.h @@ -51,7 +51,8 @@ namespace witness_complex { * Closest_landmark_range needs to have push_back operation. */ - template <typename WitnessContainer, + template <typename FiltrationValue, + typename WitnessContainer, typename LandmarkContainer, typename KNearestNeighbours> void construct_closest_landmark_table(WitnessContainer const &points, @@ -72,7 +73,8 @@ namespace witness_complex { 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], *landmarks_it), landmarks_i); + dist_i dist = std::make_pair(euclidean_distance<FiltrationValue>(points[points_i], *landmarks_it), + landmarks_i); l_heap.push(dist); } for (int i = 0; i < dim + 1; i++) { 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 new file mode 100644 index 00000000..bcb89e00 --- /dev/null +++ b/src/Witness_complex/include/gudhi/Landmark_choice_by_furthest_point.h @@ -0,0 +1,105 @@ +/* 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<double>(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 new file mode 100644 index 00000000..b5aab9d5 --- /dev/null +++ b/src/Witness_complex/include/gudhi/Landmark_choice_by_random_point.h @@ -0,0 +1,96 @@ +/* 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<double>(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/include/gudhi/Witness_complex.h b/src/Witness_complex/include/gudhi/Witness_complex.h index 489cdf11..2cec921a 100644 --- a/src/Witness_complex/include/gudhi/Witness_complex.h +++ b/src/Witness_complex/include/gudhi/Witness_complex.h @@ -72,7 +72,7 @@ class Witness_complex { typedef std::vector< Point_t > Point_Vector; typedef std::vector< Vertex_handle > typeVectorVertex; - typedef std::pair< typeVectorVertex, Filtration_value> typeSimplex; + //typedef std::pair< typeVectorVertex, Filtration_value> typeSimplex; typedef std::pair< Simplex_handle, bool > typePairSimplexBool; typedef int Witness_id; diff --git a/src/Witness_complex/test/simple_witness_complex.cpp b/src/Witness_complex/test/simple_witness_complex.cpp index 03df78ee..adaadfb0 100644 --- a/src/Witness_complex/test/simple_witness_complex.cpp +++ b/src/Witness_complex/test/simple_witness_complex.cpp @@ -33,7 +33,7 @@ #include <vector> typedef Gudhi::Simplex_tree<> Simplex_tree; -typedef std::vector< Vertex_handle > typeVectorVertex; +typedef std::vector< int > typeVectorVertex; typedef Gudhi::witness_complex::Witness_complex<Simplex_tree> WitnessComplex; BOOST_AUTO_TEST_CASE(simple_witness_complex) { diff --git a/src/Witness_complex/test/witness_complex_points.cpp b/src/Witness_complex/test/witness_complex_points.cpp index d40bbf14..b7067f87 100644 --- a/src/Witness_complex/test/witness_complex_points.cpp +++ b/src/Witness_complex/test/witness_complex_points.cpp @@ -34,7 +34,7 @@ #include <vector> typedef std::vector<double> Point; -typedef std::vector< Vertex_handle > typeVectorVertex; +typedef std::vector< int > typeVectorVertex; typedef Gudhi::Simplex_tree<> Simplex_tree; typedef Gudhi::witness_complex::Witness_complex<Simplex_tree> WitnessComplex; @@ -51,7 +51,7 @@ BOOST_AUTO_TEST_CASE(witness_complex_points) { // First test: random choice Simplex_tree complex1; Gudhi::subsampling::pick_n_random_points(points, 100, std::back_inserter(landmarks)); - Gudhi::witness_complex::construct_closest_landmark_table(points, landmarks, knn); + Gudhi::witness_complex::construct_closest_landmark_table<Simplex_tree::Filtration_value>(points, landmarks, knn); assert(!knn.empty()); WitnessComplex witnessComplex1(knn, 100, 3, complex1); BOOST_CHECK(witnessComplex1.is_witness_complex(knn, b_print_output)); |