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/* Copyright 2017-2021 PaGMO development team
This file is part of the PaGMO library.
The PaGMO library is free software; you can redistribute it and/or modify
it under the terms of either:
* the GNU Lesser General Public License as published by the Free
Software Foundation; either version 3 of the License, or (at your
option) any later version.
or
* 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.
or both in parallel, as here.
The PaGMO library 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 copies of the GNU General Public License and the
GNU Lesser General Public License along with the PaGMO library. If not,
see https://www.gnu.org/licenses/. */
#define BOOST_TEST_MODULE rastrigin_test
#define BOOST_TEST_DYN_LINK
#include <boost/test/unit_test.hpp>
#include <boost/lexical_cast.hpp>
#include <iostream>
#include <stdexcept>
#include <string>
#include <pagmo/detail/constants.hpp>
#include <pagmo/problem.hpp>
#include <pagmo/problems/rastrigin.hpp>
#include <pagmo/types.hpp>
using namespace pagmo;
BOOST_AUTO_TEST_CASE(rastrigin_test)
{
// Problem construction
rastrigin ras1{1u};
rastrigin ras5{5u};
BOOST_CHECK_THROW(rastrigin{0u}, std::invalid_argument);
BOOST_CHECK_NO_THROW(problem{rastrigin{2u}});
// Pick a few reference points
vector_double x1 = {1.};
vector_double x5 = {1., 1., 1., 1., 1.};
// Fitness test
BOOST_CHECK((ras1.fitness(x1) == vector_double{1.}));
BOOST_CHECK((ras5.fitness(x5) == vector_double{5.}));
// Gradient test
auto g1 = ras1.gradient(x1);
auto g5 = ras5.gradient(x5);
for (decltype(g1.size()) i = 0u; i < g1.size(); ++i) {
BOOST_CHECK_CLOSE(g1[i], 2., 1e-12);
}
for (decltype(g5.size()) i = 0u; i < g5.size(); ++i) {
BOOST_CHECK_CLOSE(g5[i], 2., 1e-12);
}
// Hessians test
auto h1 = ras1.hessians(x1);
auto h5 = ras5.hessians(x5);
for (decltype(h1[0].size()) i = 0u; i < h1[0].size(); ++i) {
BOOST_CHECK_CLOSE(h1[0][i], 2. + 4 * detail::pi() * detail::pi() * 10, 1e-12);
}
for (decltype(h5[0].size()) i = 0u; i < h5[0].size(); ++i) {
BOOST_CHECK_CLOSE(h5[0][i], 2. + 4 * detail::pi() * detail::pi() * 10, 1e-12);
}
// Hessian sparsity test
BOOST_CHECK((ras1.hessians_sparsity() == std::vector<sparsity_pattern>{{{0, 0}}}));
BOOST_CHECK((ras5.hessians_sparsity() == std::vector<sparsity_pattern>{{{0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}}}));
// Bounds Test
BOOST_CHECK((ras1.get_bounds() == std::pair<vector_double, vector_double>{{-5.12}, {5.12}}));
// Name and extra info tests
BOOST_CHECK(ras5.get_name().find("Rastrigin") != std::string::npos);
// Best known test
auto x_best = ras5.best_known();
BOOST_CHECK((x_best == vector_double{0., 0., 0., 0., 0.}));
}
BOOST_AUTO_TEST_CASE(rastrigin_serialization_test)
{
problem p{rastrigin{4u}};
// Call objfun to increase the internal counters.
p.fitness({1., 1., 1., 1.});
p.gradient({1., 1., 1., 1.});
p.hessians({1., 1., 1., 1.});
// Store the string representation of p.
std::stringstream ss;
auto before = boost::lexical_cast<std::string>(p);
// Now serialize, deserialize and compare the result.
{
boost::archive::binary_oarchive oarchive(ss);
oarchive << p;
}
// Change the content of p before deserializing.
p = problem{};
{
boost::archive::binary_iarchive iarchive(ss);
iarchive >> p;
}
auto after = boost::lexical_cast<std::string>(p);
BOOST_CHECK_EQUAL(before, after);
}
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