instanceBayesNet_tpl.h

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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *   SPDX-FileCopyrightText: Copyright 2005-2025                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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#pragma once
 

 
#include <agrum/PRM/instanceBayesNet.h>   // to ease IDE parser
 
namespace gum {
  namespace prm {
 
    template < typename GUM_SCALAR >
    void InstanceBayesNet< GUM_SCALAR >::_init_(const PRMInstance< GUM_SCALAR >& i) {
      for (const auto node: i.type().containerDag().nodes()) {
        try {
          // Adding the attribute
          const PRMAttribute< GUM_SCALAR >& attr = i.get(node);
          this->dag_.addNodeWithId(attr.id());
          _varNodeMap_.insert(&(attr.type().variable()), &attr);
        } catch (NotFound const&) {
          // Not an attribute
        }
      }
 
      for (const auto& arc: i.type().containerDag().arcs()) {
        try {
          this->dag_.addArc(arc.tail(), arc.head());
        } catch (InvalidNode const&) {
          // Not added means not an attribute
        }
      }
    }
 
    template < typename GUM_SCALAR >
    INLINE InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet(const PRMInstance< GUM_SCALAR >& i) :
        IBayesNet< GUM_SCALAR >(), _inst_(&i) {
      GUM_CONSTRUCTOR(InstanceBayesNet);
      _init_(i);
    }
 
    template < typename GUM_SCALAR >
    INLINE InstanceBayesNet< GUM_SCALAR >::InstanceBayesNet(const InstanceBayesNet& from) :
        IBayesNet< GUM_SCALAR >(from), _varNodeMap_(from._varNodeMap_), _inst_(from._inst_) {
      GUM_CONS_CPY(InstanceBayesNet);
    }
 
    template < typename GUM_SCALAR >
    INLINE InstanceBayesNet< GUM_SCALAR >::~InstanceBayesNet() {
      GUM_DESTRUCTOR(InstanceBayesNet);
    }
 
    template < typename GUM_SCALAR >
    INLINE InstanceBayesNet< GUM_SCALAR >&
           InstanceBayesNet< GUM_SCALAR >::operator=(const InstanceBayesNet& from) {
      if (this != &from) {
        IBayesNet< GUM_SCALAR >::operator=(from);
 
        _varNodeMap_ = from._varNodeMap_;
      }
 
      return *this;
    }
 
    template < typename GUM_SCALAR >
    INLINE const Tensor< GUM_SCALAR >& InstanceBayesNet< GUM_SCALAR >::cpt(NodeId varId) const {
      return _get_(varId).cpf();
    }
 
    template < typename GUM_SCALAR >
    INLINE const VariableNodeMap& InstanceBayesNet< GUM_SCALAR >::variableNodeMap() const {
      GUM_ERROR(NotFound, "no VariableNodeMap in an InstanceBayesNet")
    }
 
    template < typename GUM_SCALAR >
    INLINE const DiscreteVariable& InstanceBayesNet< GUM_SCALAR >::variable(NodeId id) const {
      return _get_(id).type().variable();
    }
 
    template < typename GUM_SCALAR >
    INLINE NodeId InstanceBayesNet< GUM_SCALAR >::nodeId(const DiscreteVariable& var) const {
      return _varNodeMap_[&var]->id();
    }
 
    template < typename GUM_SCALAR >
    INLINE NodeId InstanceBayesNet< GUM_SCALAR >::idFromName(const std::string& name) const {
      return _get_(name).id();
    }
 
    template < typename GUM_SCALAR >
    INLINE const DiscreteVariable&
        InstanceBayesNet< GUM_SCALAR >::variableFromName(const std::string& name) const {
      return _get_(name).type().variable();
    }
 
    template < typename GUM_SCALAR >
    INLINE const PRMClassElement< GUM_SCALAR >&
                 InstanceBayesNet< GUM_SCALAR >::_get_(NodeId id) const {
      return _inst_->get(id);
    }
 
    template < typename GUM_SCALAR >
    INLINE const PRMClassElement< GUM_SCALAR >&
                 InstanceBayesNet< GUM_SCALAR >::_get_(const std::string& name) const {
      try {
        return _inst_->get(name);
      } catch (NotFound const&) { GUM_ERROR(NotFound, "no element found with that name") }
    }
 
    template < typename GUM_SCALAR >
    INLINE const NodeProperty< Size >& InstanceBayesNet< GUM_SCALAR >::modalities() const {
      if (_modalities_.empty()) {
        for (const auto node: this->nodes()) {
          _modalities_.insert(node, variable(node).domainSize());
        }
      }
 
      return _modalities_;
    }
 
    template < typename GUM_SCALAR >
    INLINE std::string InstanceBayesNet< GUM_SCALAR >::toDot() const {
      std::string       tab = "  ";
      std::stringstream output;
      output << "digraph \"";
      output << _inst_->name() << "\" {" << std::endl;
 
      for (const auto node: this->nodes()) {
        if (this->children(node).size() > 0) {
          const NodeSet& children = this->children(node);
 
          for (const auto chi: children) {
            output << tab << "\"" << variable(node).name() << "\" -> ";
            output << "\"" << variable(chi).name() << "\";" << std::endl;
          }
        } else if (this->parents(node).size() == 0) {
          output << tab << "\"" << variable(node).name() << "\";" << std::endl;
        }
      }
 
      output << "}" << std::endl;
      return output.str();
    }
 
  } /* namespace prm */
} /* namespace gum */