/* 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): Clement Jamin * * Copyright (C) 2016 Inria * * 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 . */ #ifndef DOC_SUBSAMPLING_INTRO_SUBSAMPLING_H_ #define DOC_SUBSAMPLING_INTRO_SUBSAMPLING_H_ // needs namespace for Doxygen to link on classes namespace Gudhi { // needs namespace for Doxygen to link on classes namespace subsampling { /** \defgroup subsampling Subsampling * * \author Clément Jamin, Siargey Kachanovich * * @{ * * \section subsamplingintroduction Introduction * * This Gudhi component offers methods to subsample a set of points. * * \section sparsifyexamples Example: sparsify_point_set * * This example outputs a subset of the input points so that the * squared distance between any two points * is greater than or equal to 0.4. * * \include Subsampling/example_sparsify_point_set.cpp * * \section farthestpointexamples Example: choose_n_farthest_points * * This example outputs a subset of 100 points obtained by González algorithm, * starting with a random point. * * \include Subsampling/example_choose_n_farthest_points.cpp * * \section randompointexamples Example: pick_n_random_points * * This example outputs a subset of 100 points picked randomly. * * \include Subsampling/example_pick_n_random_points.cpp */ /** @} */ // end defgroup subsampling } // namespace subsampling } // namespace Gudhi #endif // DOC_SUBSAMPLING_INTRO_SUBSAMPLING_H_