IBNLearner_tpl.h

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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
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#pragma once
 
 
#include <algorithm>
 
#include <agrum/BN/learning/BNLearnUtils/IBNLearner.h>
 
namespace gum::learning {
 
  template < GUM_Numeric GUM_SCALAR >
  IBNLearner::Database::Database(std::string_view                  filename,
                                 const BayesNet< GUM_SCALAR >&     bn,
                                 const std::vector< std::string >& missing_symbols) {
    // assign to each column name in the database its position
    IBNLearner::isCSVFileName_(filename);
    DBInitializerFromCSV                  initializer{std::string(filename)};
    const auto&                           xvar_names = initializer.variableNames();
    std::size_t                           nb_vars    = xvar_names.size();
    HashTable< std::string, std::size_t > var_names(nb_vars);
    for (std::size_t i = std::size_t(0); i < nb_vars; ++i)
      var_names.insert(xvar_names[i], i);
 
    // we use the bn to insert the translators into the database table
    std::vector< NodeId > nodes;
    nodes.reserve(bn.internalDag().sizeNodes());
    for (const auto node: bn.internalDag())
      nodes.push_back(node);
    std::sort(nodes.begin(), nodes.end());
    std::size_t i = std::size_t(0);
    for (auto node: nodes) {
      const Variable& var = bn.variable(node);
      auto            p   = var_names.tryGet(var.name());
      if (!p) { GUM_ERROR(MissingVariableInDatabase, "Variable '" << var.name() << "' is missing") }
      _database_.insertTranslator(var, *p, missing_symbols);
      _nodeId2cols_.insert(NodeId(node), i++);
    }
 
    // fill the database
    initializer.fillDatabase(_database_);
 
    // get the domain sizes of the variables
    for (auto dom: _database_.domainSizes())
      _domain_sizes_.push_back(dom);
 
    // create the parser
    _parser_ = new DBRowGeneratorParser(_database_.handler(), DBRowGeneratorSet());
  }
 
  template < GUM_Numeric GUM_SCALAR >
  BayesNet< GUM_SCALAR > IBNLearner::Database::_BNVars_() const {
    BayesNet< GUM_SCALAR > bn;
    const std::size_t      nb_vars = _database_.nbVariables();
    for (std::size_t i = 0; i < nb_vars; ++i) {
      const DiscreteVariable& var = dynamic_cast< const DiscreteVariable& >(_database_.variable(i));
      bn.add(var);
    }
    return bn;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  IBNLearner::IBNLearner(std::string_view                   filename,
                         const gum::BayesNet< GUM_SCALAR >& bn,
                         const std::vector< std::string >&  missing_symbols) :
      scoreDatabase_(filename, bn, missing_symbols) {
    filename_     = filename;
    noPrior_      = new NoPrior(scoreDatabase_.databaseTable());
    inducedTypes_ = false;
    GUM_CONSTRUCTOR(IBNLearner);
  }
 
 
}   // namespace gum::learning