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Diffstat (limited to 'src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp')
-rw-r--r-- | src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp | 229 |
1 files changed, 229 insertions, 0 deletions
diff --git a/src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp b/src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp new file mode 100644 index 00000000..d267276c --- /dev/null +++ b/src/Alpha_complex/test/Weighted_alpha_complex_unit_test.cpp @@ -0,0 +1,229 @@ +/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + * Author(s): Vincent Rouvreau + * + * Copyright (C) 2020 Inria + * + * Modification(s): + * - YYYY/MM Author: Description of the modification + */ + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "weighted_alpha_complex" +#include <boost/test/unit_test.hpp> +#include <boost/mpl/list.hpp> + +#include <CGAL/Epick_d.h> +#include <CGAL/Epeck_d.h> + +#include <cmath> // float comparison +#include <vector> +#include <random> +#include <array> +#include <cmath> // for std::fabs + +#include <gudhi/Alpha_complex.h> +#include <gudhi/Alpha_complex_3d.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Unitary_tests_utils.h> + +using list_of_exact_kernel_variants = boost::mpl::list<CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >, + CGAL::Epeck_d< CGAL::Dimension_tag<4> > + > ; + +BOOST_AUTO_TEST_CASE_TEMPLATE(Zero_weighted_alpha_complex, Kernel, list_of_exact_kernel_variants) { + // Check that in exact mode for static dimension 4 the code for dD unweighted and for dD weighted with all weights + // 0 give exactly the same simplex tree (simplices and filtration values). + + // Random points construction + using Point_d = typename Kernel::Point_d; + std::vector<Point_d> points; + std::uniform_real_distribution<double> rd_pts(-10., 10.); + std::random_device rand_dev; + std::mt19937 rand_engine(rand_dev()); + for (int idx = 0; idx < 20; idx++) { + std::vector<double> point {rd_pts(rand_engine), rd_pts(rand_engine), rd_pts(rand_engine), rd_pts(rand_engine)}; + points.emplace_back(point.begin(), point.end()); + } + + // Alpha complex from points + Gudhi::alpha_complex::Alpha_complex<Kernel, false> alpha_complex_from_points(points); + Gudhi::Simplex_tree<> simplex; + Gudhi::Simplex_tree<>::Filtration_value infty = std::numeric_limits<Gudhi::Simplex_tree<>::Filtration_value>::infinity(); + BOOST_CHECK(alpha_complex_from_points.create_complex(simplex, infty, true)); + std::clog << "Iterator on alpha complex simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : simplex.filtration_simplex_range()) { + std::clog << " ( "; + for (auto vertex : simplex.simplex_vertex_range(f_simplex)) { + std::clog << vertex << " "; + } + std::clog << ") -> " << "[" << simplex.filtration(f_simplex) << "] " << std::endl; + } + + // Alpha complex from zero weighted points + std::vector<typename Kernel::FT> weights(20, 0.); + Gudhi::alpha_complex::Alpha_complex<Kernel, true> alpha_complex_from_zero_weighted_points(points, weights); + Gudhi::Simplex_tree<> zw_simplex; + BOOST_CHECK(alpha_complex_from_zero_weighted_points.create_complex(zw_simplex, infty, true)); + + std::clog << "Iterator on zero weighted alpha complex simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : zw_simplex.filtration_simplex_range()) { + std::clog << " ( "; + for (auto vertex : zw_simplex.simplex_vertex_range(f_simplex)) { + std::clog << vertex << " "; + } + std::clog << ") -> " << "[" << zw_simplex.filtration(f_simplex) << "] " << std::endl; + } + + BOOST_CHECK(zw_simplex == simplex); +} + +template <typename Point_d> +bool cgal_3d_point_sort (Point_d a,Point_d b) { + if (a[0] != b[0]) + return a[0] < b[0]; + if (a[1] != b[1]) + return a[1] < b[1]; + return a[2] < b[2]; +} + +BOOST_AUTO_TEST_CASE(Weighted_alpha_complex_3d_comparison) { + // check that for random weighted 3d points in safe mode the 3D and dD codes give the same result with some tolerance + + // Random points construction + using Kernel_dD = CGAL::Epeck_d< CGAL::Dimension_tag<3> >; + using Bare_point_d = typename Kernel_dD::Point_d; + using Weighted_point_d = typename Kernel_dD::Weighted_point_d; + std::vector<Weighted_point_d> w_points_d; + + using Exact_weighted_alpha_complex_3d = + Gudhi::alpha_complex::Alpha_complex_3d<Gudhi::alpha_complex::complexity::EXACT, true, false>; + using Bare_point_3 = typename Exact_weighted_alpha_complex_3d::Bare_point_3; + using Weighted_point_3 = typename Exact_weighted_alpha_complex_3d::Weighted_point_3; + std::vector<Weighted_point_3> w_points_3; + + std::uniform_real_distribution<double> rd_pts(-10., 10.); + std::uniform_real_distribution<double> rd_wghts(-0.5, 0.5); + std::random_device rand_dev; + std::mt19937 rand_engine(rand_dev()); + for (int idx = 0; idx < 20; idx++) { + std::vector<double> point {rd_pts(rand_engine), rd_pts(rand_engine), rd_pts(rand_engine)}; + double weight = rd_wghts(rand_engine); + w_points_d.emplace_back(Bare_point_d(point.begin(), point.end()), weight); + w_points_3.emplace_back(Bare_point_3(point[0], point[1], point[2]), weight); + } + + // Structures necessary for comparison + using Points = std::vector<std::array<double,3>>; + using Points_and_filtrations = std::map<Points, double>; + Points_and_filtrations pts_fltr_dD; + Points_and_filtrations pts_fltr_3d; + + // Weighted alpha complex for dD version + Gudhi::alpha_complex::Alpha_complex<Kernel_dD, true> alpha_complex_dD_from_weighted_points(w_points_d); + Gudhi::Simplex_tree<> w_simplex_d; + BOOST_CHECK(alpha_complex_dD_from_weighted_points.create_complex(w_simplex_d)); + + std::clog << "Iterator on weighted alpha complex dD simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : w_simplex_d.filtration_simplex_range()) { + Points points; + for (auto vertex : w_simplex_d.simplex_vertex_range(f_simplex)) { + CGAL::NT_converter<Kernel_dD::RT, double> cgal_converter; + Bare_point_d pt = alpha_complex_dD_from_weighted_points.get_point(vertex).point(); + points.push_back({cgal_converter(pt[0]), cgal_converter(pt[1]), cgal_converter(pt[2])}); + } + std::clog << " ( "; + std::sort (points.begin(), points.end()); + for (auto point : points) { + std::clog << point[0] << " " << point[1] << " " << point[2] << " | "; + } + std::clog << ") -> " << "[" << w_simplex_d.filtration(f_simplex) << "] "; + std::clog << std::endl; + pts_fltr_dD[points] = w_simplex_d.filtration(f_simplex); + } + + // Weighted alpha complex for 3D version + Exact_weighted_alpha_complex_3d alpha_complex_3D_from_weighted_points(w_points_3); + Gudhi::Simplex_tree<> w_simplex_3; + BOOST_CHECK(alpha_complex_3D_from_weighted_points.create_complex(w_simplex_3)); + + std::clog << "Iterator on weighted alpha complex 3D simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : w_simplex_3.filtration_simplex_range()) { + Points points; + for (auto vertex : w_simplex_3.simplex_vertex_range(f_simplex)) { + Bare_point_3 pt = alpha_complex_3D_from_weighted_points.get_point(vertex).point(); + CGAL::NT_converter<Exact_weighted_alpha_complex_3d::Kernel::RT, double> cgal_converter; + points.push_back({cgal_converter(pt[0]), cgal_converter(pt[1]), cgal_converter(pt[2])}); + } + std::clog << " ( "; + std::sort (points.begin(), points.end()); + for (auto point : points) { + std::clog << point[0] << " " << point[1] << " " << point[2] << " | "; + } + std::clog << ") -> " << "[" << w_simplex_3.filtration(f_simplex) << "] " << std::endl; + pts_fltr_3d[points] = w_simplex_d.filtration(f_simplex); + } + + // Compares structures + auto d3_itr = pts_fltr_3d.begin(); + auto dD_itr = pts_fltr_dD.begin(); + for (; d3_itr != pts_fltr_3d.end() && dD_itr != pts_fltr_dD.end(); ++d3_itr) { + if (d3_itr->first != dD_itr->first) { + for(auto point : d3_itr->first) + std::clog << point[0] << " " << point[1] << " " << point[2] << " | "; + std::clog << " versus "; + for(auto point : dD_itr->first) + std::clog << point[0] << " " << point[1] << " " << point[2] << " | "; + std::clog << std::endl; + BOOST_CHECK(false); + } + // In safe mode, relative error is less than 1e-5 (can be changed with set_relative_precision_of_to_double) + if (std::fabs(d3_itr->second - dD_itr->second) > 1e-5 * (std::fabs(d3_itr->second) + std::fabs(dD_itr->second))) { + std::clog << d3_itr->second << " versus " << dD_itr->second << " diff " + << std::fabs(d3_itr->second - dD_itr->second) << std::endl; + BOOST_CHECK(false); + } + ++dD_itr; + } +} + +using list_of_1d_kernel_variants = boost::mpl::list<CGAL::Epeck_d< CGAL::Dynamic_dimension_tag >, + CGAL::Epeck_d< CGAL::Dimension_tag<1>>, + CGAL::Epick_d< CGAL::Dynamic_dimension_tag >, + CGAL::Epick_d< CGAL::Dimension_tag<1>> + >; + +BOOST_AUTO_TEST_CASE_TEMPLATE(Weighted_alpha_complex_non_visible_points, Kernel, list_of_1d_kernel_variants) { + // check that for 2 closed weighted 1-d points, one with a high weight to hide the second one with a small weight, + // that the point with a small weight has the same high filtration value than the edge formed by the 2 points + using Point_d = typename Kernel::Point_d; + std::vector<Point_d> points; + std::vector<double> p1 {0.}; + points.emplace_back(p1.begin(), p1.end()); + // closed enough points + std::vector<double> p2 {0.1}; + points.emplace_back(p2.begin(), p2.end()); + std::vector<typename Kernel::FT> weights {100., 0.01}; + + Gudhi::alpha_complex::Alpha_complex<Kernel, true> alpha_complex(points, weights); + Gudhi::Simplex_tree<> stree; + BOOST_CHECK(alpha_complex.create_complex(stree)); + + std::clog << "Iterator on weighted alpha complex simplices in the filtration order, with [filtration value]:" + << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + std::clog << " ( "; + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::clog << vertex << " "; + } + std::clog << ") -> " << "[" << stree.filtration(f_simplex) << "] " << std::endl; + } + + BOOST_CHECK(stree.filtration(stree.find({0})) == -100.); + BOOST_CHECK(stree.filtration(stree.find({1})) == stree.filtration(stree.find({0, 1}))); + BOOST_CHECK(stree.filtration(stree.find({1})) > 100000); +}
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