classBayesNet_tpl.h

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 *       - Christophe GONZALES(_at_AMU)                                     *
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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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#pragma once
 

#include <agrum/base/core/inline.h>
#include <agrum/PRM/classBayesNet.h>   // to ease IDE parser
 
namespace gum {
  namespace prm {
 
    template < GUM_Numeric GUM_SCALAR >
    void ClassBayesNet< GUM_SCALAR >::_init_(const PRMClass< GUM_SCALAR >& c) {
      for (const auto node: c.containerDag().nodes()) {
        if (c.exists(node)) {
          // Adding the attribute
          if (PRMClassElement< GUM_SCALAR >::isAttribute(c.get(node))
              || PRMClassElement< GUM_SCALAR >::isAggregate(c.get(node))) {
            const PRMClassElement< GUM_SCALAR >& elt = c.get(node);
            this->dag_.addNodeWithId(elt.id());
            this->_varNodeMap_.insert(&(elt.type().variable()), &elt);
          }
        }
      }
 
      for (const auto& arc: c.containerDag().arcs()) {
        try {
          this->dag_.addArc(arc.tail(), arc.head());
        } catch (InvalidNode const&) {
          // Not added means not an attribute
        }
      }
    }
 
    template < GUM_Numeric GUM_SCALAR >
    ClassBayesNet< GUM_SCALAR >::ClassBayesNet(const PRMClass< GUM_SCALAR >& c) :
        IBayesNet< GUM_SCALAR >(), _class_(&c) {
      GUM_CONSTRUCTOR(ClassBayesNet);
      _init_(c);
    }
 
    template < GUM_Numeric GUM_SCALAR >
    ClassBayesNet< GUM_SCALAR >::ClassBayesNet(const ClassBayesNet< GUM_SCALAR >& from) :
        IBayesNet< GUM_SCALAR >(from), _class_(from._class_) {
      GUM_CONS_CPY(ClassBayesNet);
    }
 
    template < GUM_Numeric GUM_SCALAR >
    ClassBayesNet< GUM_SCALAR >::~ClassBayesNet() {
      GUM_DESTRUCTOR(ClassBayesNet);
    }
 
    template < GUM_Numeric GUM_SCALAR >
    ClassBayesNet< GUM_SCALAR >&
        ClassBayesNet< GUM_SCALAR >::operator=(const ClassBayesNet< GUM_SCALAR >& from) {
      if (this != &from) {
        IBayesNet< GUM_SCALAR >::operator=(from);
 
        _class_ = from._class_;
      }
 
      return *this;
    }
 
    template < GUM_Numeric GUM_SCALAR >
    const Tensor< GUM_SCALAR >& ClassBayesNet< GUM_SCALAR >::cpt(NodeId varId) const {
      return _get_(varId).cpf();
    }
 
    template < GUM_Numeric GUM_SCALAR >
    const VariableNodeMap& ClassBayesNet< GUM_SCALAR >::variableNodeMap() const {
      GUM_ERROR(FatalError, "Sorry no VarMap in a ClassBayesNet.")
    }
 
    template < GUM_Numeric GUM_SCALAR >
    const DiscreteVariable& ClassBayesNet< GUM_SCALAR >::variable(NodeId id) const {
      return _get_(id).type().variable();
    }
 
    template < GUM_Numeric GUM_SCALAR >
    NodeId ClassBayesNet< GUM_SCALAR >::nodeId(const DiscreteVariable& var) const {
      return _varNodeMap_[&var]->id();
    }
 
    template < GUM_Numeric GUM_SCALAR >
    NodeId ClassBayesNet< GUM_SCALAR >::idFromName(std::string_view name) const {
      return _get_(name).id();
    }
 
    template < GUM_Numeric GUM_SCALAR >
    const DiscreteVariable&
        ClassBayesNet< GUM_SCALAR >::variableFromName(std::string_view name) const {
      return _get_(name).type().variable();
    }
 
    template < GUM_Numeric GUM_SCALAR >
    const PRMClassElement< GUM_SCALAR >& ClassBayesNet< GUM_SCALAR >::_get_(NodeId id) const {
      if (this->dag_.exists(id)) {
        return _class_->get(id);
      } else {
        GUM_ERROR(NotFound, "no element found with that id.")
      }
    }
 
    template < GUM_Numeric GUM_SCALAR >
    const PRMClassElement< GUM_SCALAR >&
        ClassBayesNet< GUM_SCALAR >::_get_(std::string_view name) const {
      if (!_class_->exists(name)) GUM_ERROR(NotFound, "no element found with that id.")
      return _class_->get(name);
    }
 
    template < GUM_Numeric GUM_SCALAR >
    const NodeProperty< Size >& ClassBayesNet< GUM_SCALAR >::modalities() const {
      if (_modalities_.empty()) {
        for (const auto node: this->nodes()) {
          _modalities_.insert(node, (Size)variable(node).domainSize());
        }
      }
 
      return _modalities_;
    }
 
    template < GUM_Numeric GUM_SCALAR >
    std::string ClassBayesNet< GUM_SCALAR >::toDot() const {
      std::string       tab = "  ";
      std::stringstream output;
      output << std::format("digraph \"{}\" {{\n", _class_->name());
 
      for (const auto node: this->nodes()) {
        if (this->children(node).size() > 0)
          for (const auto chi: this->children(node)) {
            output << std::format("{}\"{}\" -> \"{}\";\n",
                                  tab,
                                  variable(node).name(),
                                  variable(chi).name());
          }
        else if (this->parents(node).size() == 0) {
          output << std::format("{}\"{}\";\n", tab, variable(node).name());
        }
      }
 
      output << "}\n";
      return output.str();
    }
 
  } /* namespace prm */
} /* namespace gum */