83 lines
2.9 KiB
Ruby
83 lines
2.9 KiB
Ruby
class Pagmo < Formula
|
|
desc "Scientific library for massively parallel optimization"
|
|
homepage "https://esa.github.io/pagmo2/"
|
|
url "https://github.com/esa/pagmo2/archive/v2.18.0.tar.gz"
|
|
sha256 "5ad40bf3aa91857a808d6b632d9e1020341a33f1a4115d7a2b78b78fd063ae31"
|
|
license any_of: ["LGPL-3.0-or-later", "GPL-3.0-or-later"]
|
|
revision 1
|
|
|
|
bottle do
|
|
sha256 cellar: :any, arm64_monterey: "9fdeb12b0f2606287dd1e2032da47a63c84702b3b983ea502fa258ef8ccd2c46"
|
|
sha256 cellar: :any, arm64_big_sur: "c47c61282421e3ca6dcc9b0c3506860d79283a3612eb2307f52292c48e423986"
|
|
sha256 cellar: :any, monterey: "76a1ef6baf81a36641d9ded41378f20f4a528f378e289d9d4c03396143fd49bc"
|
|
sha256 cellar: :any, big_sur: "3c13ea41cb30df2fdb763fce03b402ee38c0a44a48eb41374dda972ca19786f0"
|
|
sha256 cellar: :any, catalina: "5ec05fbd452a6bd80feaab0e4030ad8e2ff5d29e5e895779171118ea7bada8ed"
|
|
sha256 cellar: :any_skip_relocation, x86_64_linux: "167678b868dc7796bbe34aa805d657b2012bcf6c413594e1dbff9e190dbe57fb"
|
|
end
|
|
|
|
depends_on "cmake" => :build
|
|
depends_on "boost"
|
|
depends_on "eigen"
|
|
depends_on "nlopt"
|
|
depends_on "tbb"
|
|
|
|
on_linux do
|
|
depends_on "gcc"
|
|
end
|
|
|
|
fails_with gcc: "5"
|
|
|
|
def install
|
|
system "cmake", ".", "-DPAGMO_WITH_EIGEN3=ON", "-DPAGMO_WITH_NLOPT=ON",
|
|
*std_cmake_args,
|
|
"-DCMAKE_CXX_STANDARD=17"
|
|
system "make", "install"
|
|
end
|
|
|
|
test do
|
|
(testpath/"test.cpp").write <<~EOS
|
|
#include <iostream>
|
|
|
|
#include <pagmo/algorithm.hpp>
|
|
#include <pagmo/algorithms/sade.hpp>
|
|
#include <pagmo/archipelago.hpp>
|
|
#include <pagmo/problem.hpp>
|
|
#include <pagmo/problems/schwefel.hpp>
|
|
|
|
using namespace pagmo;
|
|
|
|
int main()
|
|
{
|
|
// 1 - Instantiate a pagmo problem constructing it from a UDP
|
|
// (i.e., a user-defined problem, in this case the 30-dimensional
|
|
// generalised Schwefel test function).
|
|
problem prob{schwefel(30)};
|
|
|
|
// 2 - Instantiate a pagmo algorithm (self-adaptive differential
|
|
// evolution, 100 generations).
|
|
algorithm algo{sade(100)};
|
|
|
|
// 3 - Instantiate an archipelago with 16 islands having each 20 individuals.
|
|
archipelago archi{16u, algo, prob, 20u};
|
|
|
|
// 4 - Run the evolution in parallel on the 16 separate islands 10 times.
|
|
archi.evolve(10);
|
|
|
|
// 5 - Wait for the evolutions to finish.
|
|
archi.wait_check();
|
|
|
|
// 6 - Print the fitness of the best solution in each island.
|
|
for (const auto &isl : archi) {
|
|
std::cout << isl.get_population().champion_f()[0] << std::endl;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
EOS
|
|
|
|
system ENV.cxx, "test.cpp", "-I#{include}", "-L#{lib}", "-lpagmo",
|
|
"-std=c++17", "-o", "test"
|
|
system "./test"
|
|
end
|
|
end
|