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test_dnn_ff.cpp
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test_dnn_ff.cpp
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//| This file is a part of the sferes2 framework.
//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
//| Main contributor(s): Jean-Baptiste Mouret, [email protected]
//|
//| This software is a computer program whose purpose is to facilitate
//| experiments in evolutionary computation and evolutionary robotics.
//|
//| This software is governed by the CeCILL license under French law
//| and abiding by the rules of distribution of free software. You
//| can use, modify and/ or redistribute the software under the terms
//| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
//| following URL "http://www.cecill.info".
//|
//| As a counterpart to the access to the source code and rights to
//| copy, modify and redistribute granted by the license, users are
//| provided only with a limited warranty and the software's author,
//| the holder of the economic rights, and the successive licensors
//| have only limited liability.
//|
//| In this respect, the user's attention is drawn to the risks
//| associated with loading, using, modifying and/or developing or
//| reproducing the software by the user in light of its specific
//| status of free software, that may mean that it is complicated to
//| manipulate, and that also therefore means that it is reserved for
//| developers and experienced professionals having in-depth computer
//| knowledge. Users are therefore encouraged to load and test the
//| software's suitability as regards their requirements in conditions
//| enabling the security of their systems and/or data to be ensured
//| and, more generally, to use and operate it in the same conditions
//| as regards security.
//|
//| The fact that you are presently reading this means that you have
//| had knowledge of the CeCILL license and that you accept its terms.
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE dnn_ff
#include <boost/archive/xml_oarchive.hpp>
#include <boost/archive/xml_iarchive.hpp>
#include <boost/archive/binary_oarchive.hpp>
#include <boost/archive/binary_iarchive.hpp>
#include <boost/test/unit_test.hpp>
#include <boost/serialization/nvp.hpp>
#include <iostream>
#include <cmath>
#include <algorithm>
#include <boost/graph/depth_first_search.hpp>
#include <sferes/fit/fitness.hpp>
#include <sferes/gen/evo_float.hpp>
#include <sferes/phen/parameters.hpp>
#include "gen_dnn_ff.hpp"
#include "phen_dnn.hpp"
using namespace sferes;
using namespace sferes::gen::dnn;
using namespace sferes::gen::evo_float;
struct Params {
struct evo_float {
SFERES_CONST float mutation_rate = 0.1f;
SFERES_CONST float cross_rate = 0.1f;
SFERES_CONST mutation_t mutation_type = polynomial;
SFERES_CONST cross_over_t cross_over_type = sbx;
SFERES_CONST float eta_m = 15.0f;
SFERES_CONST float eta_c = 15.0f;
};
struct parameters {
// maximum value of parameters
SFERES_CONST float min = -5.0f;
// minimum value
SFERES_CONST float max = 5.0f;
};
struct dnn {
SFERES_CONST size_t nb_inputs = 4;
SFERES_CONST size_t nb_outputs = 2;
SFERES_CONST size_t min_nb_neurons = 4;
SFERES_CONST size_t max_nb_neurons = 5;
SFERES_CONST size_t min_nb_conns = 100;
SFERES_CONST size_t max_nb_conns = 101;
SFERES_CONST float m_rate_add_conn = 1.0f;
SFERES_CONST float m_rate_del_conn = 0.1f;
SFERES_CONST float m_rate_change_conn = 1.0f;
SFERES_CONST float m_rate_add_neuron = 1.0f;
SFERES_CONST float m_rate_del_neuron = 1.0f;
SFERES_CONST int io_param_evolving = true;
SFERES_CONST init_t init = ff;
};
};
struct cycle_detector : public boost::dfs_visitor<> {
cycle_detector(bool& has_cycle)
: m_has_cycle(has_cycle) { }
template <class Edge, class Graph>
void back_edge(Edge, Graph&) {
m_has_cycle = true;
}
protected:
bool& m_has_cycle;
};
BOOST_AUTO_TEST_CASE(direct_nn_ff) {
srand(time(0));
typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> weight_t;
typedef phen::Parameters<gen::EvoFloat<1, Params>, fit::FitDummy<>, Params> bias_t;
typedef nn::PfWSum<weight_t> pf_t;
typedef nn::AfTanh<bias_t> af_t;
typedef nn::Neuron<pf_t, af_t> neuron_t;
typedef nn::Connection<weight_t> connection_t;
typedef gen::DnnFF<neuron_t, connection_t, Params> gen_t;
typedef phen::Dnn<gen_t, fit::FitDummy<Params>, Params> phen_t;
phen_t i;
i.random();
i.develop();
std::vector<float> in(4);
std::fill(in.begin(), in.end(), 0);
i.nn().step(in);
BOOST_CHECK_EQUAL(i.nn().get_nb_inputs(), 4);
BOOST_CHECK_EQUAL(i.nn().get_nb_outputs(), 2);
BOOST_CHECK_EQUAL(i.nn().get_nb_neurons(), 6);
BOOST_CHECK_EQUAL(i.nn().get_nb_connections(), 8);
std::ofstream ofs("/tmp/nn.dot");
i.nn().write(ofs);
for (size_t k = 0; k < 40; ++k)
i.mutate();
std::ofstream ofs2("/tmp/nn2.dot");
i.nn().write(ofs2);
bool has_cycle = false;
cycle_detector vis(has_cycle);
boost::depth_first_search(i.nn().get_graph(),
boost::color_map(get(&phen_t::nn_t::neuron_t::_color,
i.nn().get_graph())).visitor(vis));
BOOST_CHECK(!has_cycle);
}