O3prmBNWriter_tpl.h

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/***************************************************************************
 *   Copyright (C) 2005 by Pierre-Henri WUILLEMIN et Christophe GONZALES   *
 *   {prenom.nom}_at_lip6.fr                                               *
 *                                                                         *
 *   This program is free software; you can redistribute it and/or modify  *
 *   it under the terms of the GNU General Public License as published by  *
 *   the Free Software Foundation; either version 2 of the License, or     *
 *   (at your option) any later version.                                   *
 *                                                                         *
 *   This program 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 a copy of the GNU General Public License     *
 *   along with this program; if not, write to the                         *
 *   Free Software Foundation, Inc.,                                       *
 *   59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.             *
 ***************************************************************************/

#ifndef DOXYGEN_SHOULD_SKIP_THIS

#  include <agrum/PRM/o3prm/O3prmBNWriter.h>

#  define O3PRM_INDENT "  "

namespace gum {
  /*
   * Default constructor.
   */
  template < typename GUM_SCALAR >
  INLINE O3prmBNWriter< GUM_SCALAR >::O3prmBNWriter() {
    GUM_CONSTRUCTOR(O3prmBNWriter);
  }

  /*
   * Destructor.
   */
  template < typename GUM_SCALAR >
  INLINE O3prmBNWriter< GUM_SCALAR >::~O3prmBNWriter() {
    GUM_DESTRUCTOR(O3prmBNWriter);
  }

  /*
   * Writes a bayes net in the given ouput stream.
   *
   * @param output The output stream.
   * @param bn The bayes net writen in the stream.
   * @throws IOError Raised if an I/O error occurs.
   */
  template < typename GUM_SCALAR >
  INLINE void
     O3prmBNWriter< GUM_SCALAR >::write(std::ostream&                  output,
                                        const IBayesNet< GUM_SCALAR >& bn) {
    if (!output.good()) {
      GUM_ERROR(IOError, "Stream states flags are not all unset.");
    }

    output << "class BayesNet {" << std::endl;

    for (auto node : bn.nodes()) {
      output << __extractAttribute(bn, node) << std::endl;
    }

    output << "}" << std::endl;

    output << std::endl;

    output.flush();

    if (output.fail()) { GUM_ERROR(IOError, "Writing in the ostream failed."); }
  }

  template < typename GUM_SCALAR >
  INLINE std::string O3prmBNWriter< GUM_SCALAR >::__extractAttribute(
     const IBayesNet< GUM_SCALAR >& bn, NodeId node) {
    std::stringstream str;
    str << O3PRM_INDENT;
    str << __extractType(bn, node) << " ";
    str << __extractName(bn, node) << " ";
    if (bn.parents(node).size() > 0) {
      str << "dependson " << __extractParents(bn, node) << " ";
    }
    str << " {" << __extractCPT(bn, node) << "};" << std::endl;
    return str.str();
  }

  template < typename GUM_SCALAR >
  INLINE std::string O3prmBNWriter< GUM_SCALAR >::__extractParents(
     const IBayesNet< GUM_SCALAR >& bn, NodeId node) {
    std::stringstream str;
    auto              var = &(bn.variable(node));
    for (auto parent : bn.cpt(node).variablesSequence()) {
      if (var != parent) { str << parent->name() << ", "; }
    }
    return str.str().substr(0, str.str().size() - 2);
  }

  template < typename GUM_SCALAR >
  INLINE std::string
         O3prmBNWriter< GUM_SCALAR >::__extractCPT(const IBayesNet< GUM_SCALAR >& bn,
                                               NodeId node) {
    std::stringstream str;
    bool              first = true;
    Instantiation     inst(bn.cpt(node));

    str << "[";
    if (inst.nbrDim() == 1) {
      // 1D potential
      for (inst.setFirst(); !inst.end(); inst.inc()) {
        if (!first) {
          str << ", ";
        } else {
          first = false;
        }
        str << bn.cpt(node)[inst];
      }
    } else {
      // (>1)D potential (with parents)
      Instantiation jnst;
      for (auto var = inst.variablesSequence().rbegin();
           var != inst.variablesSequence().rend();
           --var) {
        jnst.add(**var);
      }
      inst.setFirst();
      auto currentval = inst.val(0) + 1;
      for (jnst.setFirst(); !jnst.end(); jnst.inc()) {
        inst.setVals(jnst);
        if (!first) {
          str << ", ";
        } else {
          first = false;
        }
        if (currentval != inst.val(0)) {   // begining line
          str << std::endl << O3PRM_INDENT << O3PRM_INDENT;
          currentval = inst.val(0);
        }
        str << bn.cpt(node)[inst];
      }
      str << std::endl << O3PRM_INDENT;
    }

    str << "]";
    return str.str();
  }

  template < typename GUM_SCALAR >
  INLINE std::string
         O3prmBNWriter< GUM_SCALAR >::__extractType(const IBayesNet< GUM_SCALAR >& bn,
                                                NodeId node) {
    switch (bn.variable(node).varType()) {
      case gum::VarType::Discretized: {
        auto double_var = dynamic_cast< const DiscretizedVariable< double >* >(
           &(bn.variable(node)));
        if (double_var != nullptr) {
          return __extractDiscretizedType< DiscretizedVariable< double > >(
             double_var);
        } else {
          auto float_var = dynamic_cast< const DiscretizedVariable< float >* >(
             &(bn.variable(node)));
          if (float_var != nullptr) {
            return __extractDiscretizedType< DiscretizedVariable< float > >(
               float_var);
          }
        }
        GUM_ERROR(InvalidArgument,
                  "DiscretizedVariable ticks are neither doubles or floats");
      }
      case gum::VarType::Range: {
        return __extractRangeType(bn, node);
      }
      default: { return __extractLabelizedType(bn, node); }
    }
  }

  template < typename GUM_SCALAR >
  INLINE std::string O3prmBNWriter< GUM_SCALAR >::__extractRangeType(
     const IBayesNet< GUM_SCALAR >& bn, NodeId node) {
    const auto&       var = static_cast< const RangeVariable& >(bn.variable(node));
    std::stringstream str;
    str << "int (" << var.minVal() << ", " << var.maxVal() << ")";
    return str.str();
  }

  template < typename GUM_SCALAR >
  INLINE std::string O3prmBNWriter< GUM_SCALAR >::__extractLabelizedType(
     const IBayesNet< GUM_SCALAR >& bn, NodeId node) {
    std::stringstream str;
    str << "labels(";
    for (auto l : bn.variable(node).labels()) {
      str << l << ", ";
    }
    return str.str().substr(0, str.str().size() - 2) + ")";
  }

  template < typename GUM_SCALAR >
  template < typename VARTYPE >
  INLINE std::string
         O3prmBNWriter< GUM_SCALAR >::__extractDiscretizedType(const VARTYPE* var) {
    std::stringstream str;
    if (var->ticks().size() >= 3) {
      str << "real(" << var->ticks()[0];
      for (size_t i = 1; i < var->ticks().size(); ++i) {
        str << ", " << var->ticks()[i];
      }
      str << ")";
      return str.str();
    }
    GUM_ERROR(InvalidArgument, "discretized variable does not have enough ticks");
  }

  template < typename GUM_SCALAR >
  INLINE std::string
         O3prmBNWriter< GUM_SCALAR >::__extractName(const IBayesNet< GUM_SCALAR >& bn,
                                                NodeId node) {
    if (!bn.variable(node).name().empty()) {
      return bn.variable(node).name();
    } else {
      std::stringstream str;
      str << node;
      return str.str();
    }
  }

  /*
   * Writes a bayes net in the file referenced by filePath.
   * If the file doesn't exists, it is created.
   * If the file exists, it's content will be erased.
   *
   * @param filePath The path to the file used to write the bayes net.
   * @param bn The bayes net writen in the file.
   * @throw IOError Raised if an I/O error occurs.
   */
  template < typename GUM_SCALAR >
  INLINE void
     O3prmBNWriter< GUM_SCALAR >::write(const std::string&             filePath,
                                        const IBayesNet< GUM_SCALAR >& bn) {
    std::ofstream output(filePath.c_str(), std::ios_base::trunc);

    write(output, bn);

    output.close();

    if (output.fail()) { GUM_ERROR(IOError, "Writing in the ostream failed."); }
  }

} /* namespace gum */

#endif   // DOXYGEN_SHOULD_SKIP_THIS