DBRowGeneratorSet_tpl.h

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 *   Copyright (c) 2005-2025 by                                             *
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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
 

 
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
namespace gum {
 
  namespace learning {

    template < class Generator >
    void DBRowGeneratorSet::insertGenerator(const Generator& generator) {
      // check that no output row generation is still active
      if (_output_row_ != nullptr)
        GUM_ERROR(OperationNotAllowed,
                  "you cannot insert a new generator while a generation is "
                  "still being processed");
 
      _generators_.push_back(generator.clone());
 
      try {
        _setInputRow_performed_.push_back(0);
      } catch (...) {
        delete _generators_.back();
        _generators_.pop_back();
        throw;
      }
 
      ++_nb_generators_;
      _output_row_ = nullptr;
    }

    template < class Generator >
    void DBRowGeneratorSet::insertGenerator(const Generator& generator, const std::size_t i) {
      // check that no output row generation is still active
      if (_output_row_ != nullptr)
        GUM_ERROR(OperationNotAllowed,
                  "you cannot insert a new generator while a generation is "
                  "still being processed");
 
      _generators_.insert(_generators_.begin() + i, generator.clone());
 
      try {
        _setInputRow_performed_.push_back(0);
      } catch (...) {
        delete *(_generators_.begin() + i);
        _generators_.erase(_generators_.begin() + i);
        throw;
      }
 
      ++_nb_generators_;
      _output_row_ = nullptr;
    }

    template < typename GUM_SCALAR >
    void DBRowGeneratorSet::setBayesNet(const BayesNet< GUM_SCALAR >& new_bn) {
      HashTable< DBRowGeneratorWithBN< GUM_SCALAR >*, const BayesNet< GUM_SCALAR >* > old_bns;
 
      for (auto xgen: _generators_) {
        // check if the generator relies on a Bayes net
        DBRowGeneratorWithBN< GUM_SCALAR >* gen = nullptr;
        try {
          gen = dynamic_cast< DBRowGeneratorWithBN< GUM_SCALAR >* >(xgen);
        } catch (std::bad_cast&) {}
 
        if (gen != nullptr) {
          // try to assign the new BN to the generator
          try {
            const BayesNet< GUM_SCALAR >* bn = &(gen->getBayesNet());
            old_bns.insert(gen, bn);
            gen->setBayesNet(new_bn);
          } catch (...) {
            // if we could not assign the new BN to the generator, then
            // make all the generators that were successfully assigned this
            // BN revert to the old BN they had
            for (auto& generator: old_bns) {
              generator.first->setBayesNet(*(generator.second));
            }
            throw;
          }
        }
      }
    }
 
  } /* namespace learning */
 
} /* namespace gum */
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */