gen_dnn_ff.hpp

//| This file is a part of the sferes2 framework.
//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
//|
//| 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".
//|
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#ifndef DNN_FF_HPP_
#define DNN_FF_HPP_

#include <modules/nn2/gen_dnn.hpp>
#include <boost/graph/dijkstra_shortest_paths.hpp>
#include <boost/graph/dag_shortest_paths.hpp>
#include <boost/graph/visitors.hpp>
#include <boost/graph/reverse_graph.hpp>
#include <boost/property_map/vector_property_map.hpp>

namespace sferes {
  namespace gen {
    template<typename N, typename C, typename Params>
    class DnnFF : public Dnn<N, C, Params> {
     public:
      typedef nn::NN<N, C> nn_t;
      typedef N neuron_t;
      typedef C conn_t;
      typedef typename nn_t::io_t io_t;
      typedef typename nn_t::vertex_desc_t vertex_desc_t;
      typedef typename nn_t::edge_desc_t edge_desc_t;
      typedef typename nn_t::graph_t graph_t;
      DnnFF() {}
      DnnFF& operator=(const DnnFF& o) {
        static_cast<nn::NN<N, C>& >(*this)
          = static_cast<const nn::NN<N, C>& >(o);
        return *this;
      }
      DnnFF(const DnnFF& o) {
        *this = o;
      }
      void init() {
        Dnn<N, C, Params>::init();
        _compute_depth();
      }
      void random() {
        assert(Params::dnn::init == dnn::ff);
        this->_random_ff(Params::dnn::nb_inputs, Params::dnn::nb_outputs);
        _make_all_vertices();
      }
      void mutate() {
        _change_conns();
        this->_change_neurons();

        if (misc::rand<float>() < Params::dnn::m_rate_add_conn)
          _add_conn();

        if (misc::rand<float>() < Params::dnn::m_rate_del_conn)
          this->_del_conn();

        if (misc::rand<float>() < Params::dnn::m_rate_add_neuron)
          this->_add_neuron_on_conn();

        if (misc::rand<float>() < Params::dnn::m_rate_del_neuron)
          this->_del_neuron();
      }

      void cross(const DnnFF& o, DnnFF& c1, DnnFF& c2) {
        if (misc::flip_coin()) {
          c1 = *this;
          c2 = o;
        } else {
          c2 = *this;
          c1 = o;
        }
      }
      size_t get_depth() const {
        return _depth;
      }
     protected:
      std::set<vertex_desc_t> _all_vertices;
      size_t _depth;

      void _make_all_vertices() {
        _all_vertices.clear();
        BGL_FORALL_VERTICES_T(v, this->_g, graph_t)
        _all_vertices.insert(v);
      }
      void _change_conns() {
        BGL_FORALL_EDGES_T(e, this->_g, graph_t)
        this->_g[e].get_weight().mutate();
      }


      // add only feed-forward connections
      void _add_conn() {
        using namespace boost;
        vertex_desc_t v = this->_random_src();
        std::set<vertex_desc_t> preds;
        nn::bfs_pred_visitor<vertex_desc_t> vis(preds);
        breadth_first_search(make_reverse_graph(this->_g),
                             v, color_map(get(&N::_color, this->_g)).visitor(vis));
        _make_all_vertices();
        std::set<vertex_desc_t> tmp, avail, in;
        // avoid to connect to predecessors
        std::set_difference(_all_vertices.begin(), _all_vertices.end(),
                            preds.begin(), preds.end(),
                            std::insert_iterator<std::set<vertex_desc_t> >(tmp, tmp.begin()));
        // avoid to connect to inputs
        BOOST_FOREACH(vertex_desc_t v, this->_inputs) // inputs need
        // to be sorted
        in.insert(v);
        std::set_difference(tmp.begin(), tmp.end(),
                            in.begin(), in.end(),
                            std::insert_iterator<std::set<vertex_desc_t> >(avail, avail.begin()));

        if (avail.empty())
          return;
        vertex_desc_t tgt = *misc::rand_l(avail);
        typename nn_t::weight_t w;
        w.random();
        this->add_connection(v, tgt, w);
      }

      // useful to make the right number of steps
      void _compute_depth() {
        using namespace boost;
        typedef std::map<vertex_desc_t, size_t> int_map_t;
        typedef std::map<vertex_desc_t, vertex_desc_t> vertex_map_t;
        typedef std::map<vertex_desc_t, default_color_type> color_map_t;
        typedef std::map<edge_desc_t, int> edge_map_t;

        typedef associative_property_map<int_map_t> a_map_t;
        typedef associative_property_map<color_map_t> c_map_t;
        typedef associative_property_map<vertex_map_t> v_map_t;
        typedef associative_property_map<edge_map_t> e_map_t;

        color_map_t cm;
        c_map_t cmap(cm);
        vertex_map_t vm;
        v_map_t pmap(vm);
        edge_map_t em;
        BGL_FORALL_EDGES_T(e, this->_g, graph_t)
        em[e] = 1;
        e_map_t wmap(em);
        _depth = 0;
        // we compute the longest path between inputs and outputs
        BOOST_FOREACH(vertex_desc_t s, this->_inputs) {
          int_map_t im;
          a_map_t dmap(im);
          dag_shortest_paths
          (this->_g, s, dmap, wmap, cmap, pmap,
           dijkstra_visitor<null_visitor>(),
           std::greater<int>(),
           closed_plus<int>(),
           std::numeric_limits<int>::min(), 0);

          BGL_FORALL_VERTICES_T(v, this->_g, graph_t) {
            size_t d = get(dmap, v);
            if (this->_g[v].get_out() != -1 && d <= num_vertices(this->_g))
              _depth = std::max(_depth, d);
          }
        }
        // add one to be sure
        _depth ++;
      }

    };

  }
}

#endif