From d6424d2c288a01bb6ab6e660d3ed75eee94bfce9 Mon Sep 17 00:00:00 2001 From: Andreas Fabri Date: Tue, 21 May 2024 17:28:07 +0100 Subject: [PATCH] merge conflicts --- .../random-forest/common-libraries.hpp | 1 - .../Expectation_maximization_test.cpp | 19 +++++++++---------- .../K_means_clustering_test.cpp | 19 +++++++++---------- 3 files changed, 18 insertions(+), 21 deletions(-) diff --git a/Classification/include/CGAL/Classification/ETHZ/internal/random-forest/common-libraries.hpp b/Classification/include/CGAL/Classification/ETHZ/internal/random-forest/common-libraries.hpp index c62a2cddfcae..bb38f5eebd82 100644 --- a/Classification/include/CGAL/Classification/ETHZ/internal/random-forest/common-libraries.hpp +++ b/Classification/include/CGAL/Classification/ETHZ/internal/random-forest/common-libraries.hpp @@ -36,7 +36,6 @@ #include #include #include -#include #if defined(CGAL_LINKED_WITH_BOOST_IOSTREAMS) && defined(CGAL_LINKED_WITH_BOOST_SERIALIZATION) #include #endif diff --git a/Surface_mesh_segmentation/test/Surface_mesh_segmentation/Expectation_maximization_test.cpp b/Surface_mesh_segmentation/test/Surface_mesh_segmentation/Expectation_maximization_test.cpp index 2e0d58a401db..6164792b6c5b 100644 --- a/Surface_mesh_segmentation/test/Surface_mesh_segmentation/Expectation_maximization_test.cpp +++ b/Surface_mesh_segmentation/test/Surface_mesh_segmentation/Expectation_maximization_test.cpp @@ -2,7 +2,6 @@ #include #include -#include /** * Generates sample points using a few gauissians. * Then applies gmm fitting on these generated points. @@ -15,16 +14,16 @@ int main(void) engine.seed(1340818006); // generate random data using gauissians below - std::vector< boost::normal_distribution > distributions; - distributions.push_back(boost::normal_distribution(0.1, 0.05)); - distributions.push_back(boost::normal_distribution(0.4, 0.1)); - distributions.push_back(boost::normal_distribution(0.55, 0.05)); - distributions.push_back(boost::normal_distribution(0.7, 0.1)); - distributions.push_back(boost::normal_distribution(0.9, 0.05)); - distributions.push_back(boost::normal_distribution(1.0, 0.05)); + std::vector< std::normal_distribution > distributions; + distributions.push_back(std::normal_distribution(0.1, 0.05)); + distributions.push_back(std::normal_distribution(0.4, 0.1)); + distributions.push_back(std::normal_distribution(0.55, 0.05)); + distributions.push_back(std::normal_distribution(0.7, 0.1)); + distributions.push_back(std::normal_distribution(0.9, 0.05)); + distributions.push_back(std::normal_distribution(1.0, 0.05)); std::vector data; - for(std::vector< boost::normal_distribution >::iterator it = distributions.begin(); + for(std::vector< std::normal_distribution >::iterator it = distributions.begin(); it != distributions.end(); ++it) { @@ -39,7 +38,7 @@ int main(void) { std::size_t center_id = (std::numeric_limits::max)(), center_counter = 0;; double min_distance = (std::numeric_limits::max)(); - for(std::vector< boost::normal_distribution >::iterator dis_it = distributions.begin(); + for(std::vector< std::normal_distribution >::iterator dis_it = distributions.begin(); dis_it != distributions.end(); ++dis_it, ++center_counter) { double distance = std::abs(*it - dis_it->mean()); diff --git a/Surface_mesh_segmentation/test/Surface_mesh_segmentation/K_means_clustering_test.cpp b/Surface_mesh_segmentation/test/Surface_mesh_segmentation/K_means_clustering_test.cpp index d55c419adaeb..36762b2a0f20 100644 --- a/Surface_mesh_segmentation/test/Surface_mesh_segmentation/K_means_clustering_test.cpp +++ b/Surface_mesh_segmentation/test/Surface_mesh_segmentation/K_means_clustering_test.cpp @@ -2,7 +2,6 @@ #include #include -#include /** * Generates sample points using a few gauissians. * Then applies k-means on these generated points. @@ -16,16 +15,16 @@ int main(void) engine.seed(1340818006); // generate random data using gauissians below - std::vector< boost::normal_distribution > distributions; - distributions.push_back(boost::normal_distribution(0.1, 0.05)); - distributions.push_back(boost::normal_distribution(0.4, 0.1)); - distributions.push_back(boost::normal_distribution(0.55, 0.05)); - distributions.push_back(boost::normal_distribution(0.7, 0.1)); - distributions.push_back(boost::normal_distribution(0.9, 0.05)); - distributions.push_back(boost::normal_distribution(1.0, 0.05)); + std::vector< std::normal_distribution > distributions; + distributions.push_back(std::normal_distribution(0.1, 0.05)); + distributions.push_back(std::normal_distribution(0.4, 0.1)); + distributions.push_back(std::normal_distribution(0.55, 0.05)); + distributions.push_back(std::normal_distribution(0.7, 0.1)); + distributions.push_back(std::normal_distribution(0.9, 0.05)); + distributions.push_back(std::normal_distribution(1.0, 0.05)); std::vector data; - for(std::vector< boost::normal_distribution >::iterator it = distributions.begin(); + for(std::vector< std::normal_distribution >::iterator it = distributions.begin(); it != distributions.end(); ++it) { for(std::size_t i = 0; i < 300; ++i) { data.push_back((*it)(engine)); } @@ -38,7 +37,7 @@ int main(void) { std::size_t center_id = (std::numeric_limits::max)(), center_counter = 0;; double min_distance = (std::numeric_limits::max)(); - for(std::vector< boost::normal_distribution >::iterator dis_it = distributions.begin(); + for(std::vector< std::normal_distribution >::iterator dis_it = distributions.begin(); dis_it != distributions.end(); ++dis_it, ++center_counter) { double distance = std::abs(*it - dis_it->mean());