localSearchWithTabuList_tpl.h

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

 
#include <agrum/BN/learning/localSearchWithTabuList.h>   // to ease IDE parser
#include <agrum/BN/learning/paramUtils/DAG2BNLearner.h>
#include <agrum/BN/learning/structureUtils/graphChange.h>
 
namespace gum {
 
  namespace learning {

    template < typename GRAPH_CHANGES_SELECTOR >
    DAG LocalSearchWithTabuList::learnStructure(GRAPH_CHANGES_SELECTOR& selector, DAG dag) {
      selector.setGraph(dag);
 
      unsigned int nb_changes_applied                 = 0;
      Idx          applied_change_with_positive_score = 0;
      Idx          current_N                          = 0;
 
      initApproximationScheme();
 
      // the best dag found so far with its score
      DAG    best_dag      = dag;
      double best_score    = 0;
      double current_score = 0;
      double delta_score   = 0;
 
      do {
        applied_change_with_positive_score = 0;
        delta_score                        = 0;
 
        try {
          const auto& change = selector.bestChange();
          delta_score        = selector.deltaScore(change, true);
 
          if ((nb_changes_applied > 0) || (delta_score > 0)) {
            if (delta_score > 0) {
              ++applied_change_with_positive_score;
            } else if (current_score > best_score) {
              best_score = current_score;
              best_dag   = dag;
            }
 
            selector.applyChange(change);
            current_score += delta_score;
            ++nb_changes_applied;
          }
 
          updateApproximationScheme(nb_changes_applied);
 
          // update current_N
          if (applied_change_with_positive_score) {
            current_N          = 0;
            nb_changes_applied = 0;
          } else {
            ++current_N;
          }
        } catch (NotFound&) { break; }
      } while ((current_N <= _MaxNbDecreasing_) && continueApproximationScheme(delta_score));
 
      stopApproximationScheme();   // just to be sure of the approximationScheme
                                   // has been notified of the end of loop
 
      // get the dag that we will return
      auto& res_dag = current_score > best_score ? dag : best_dag;
 
      // here, we add to the dag the set of nodes that were removed by method
      // setGraph because they did not belong to the database
      selector.finalizeGraph(res_dag);
 
      return res_dag;
    }

    template < GUM_Numeric GUM_SCALAR, typename GRAPH_CHANGES_SELECTOR, typename PARAM_ESTIMATOR >
    BayesNet< GUM_SCALAR > LocalSearchWithTabuList::learnBN(GRAPH_CHANGES_SELECTOR& selector,
                                                            PARAM_ESTIMATOR&        estimator,
                                                            DAG                     initial_dag) {
      return DAG2BNLearner::createBN< GUM_SCALAR >(estimator,
                                                   learnStructure(selector, initial_dag));
    }
 
  } /* namespace learning */
 
} /* namespace gum */