greedyHillClimbing_tpl.h

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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
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#pragma once
 

 
#include <agrum/BN/learning/paramUtils/DAG2BNLearner.h>
#include <agrum/BN/learning/structureUtils/graphChange.h>
 
namespace gum {
 
  namespace learning {

    template < typename GRAPH_CHANGES_SELECTOR >
    DAG GreedyHillClimbing::learnStructure(GRAPH_CHANGES_SELECTOR& selector, DAG dag) {
      selector.setGraph(dag);
 
      unsigned int nb_changes_applied = 1;
      double       delta_score;
 
      initApproximationScheme();
 
      // a vector that indicates which queues have valid scores, i.e., scores
      // that were not invalidated by previously applied changes
      std::vector< bool > impacted_queues(dag.size(), false);
 
      do {
        nb_changes_applied = 0;
        delta_score        = 0;
 
        std::vector< std::pair< NodeId, double > > ordered_queues
            = selector.nodesSortedByBestScore();
 
        for (Idx j = 0; j < dag.size(); ++j) {
          Idx i = ordered_queues[j].first;
 
          if (!(selector.empty(i)) && (selector.bestScore(i) > 0)) {
            // pick up the best change
            const GraphChange& change = selector.bestChange(i);
 
            // perform the change
            switch (change.type()) {
              case GraphChangeType::ARC_ADDITION :
                if (!impacted_queues[change.node2()] && selector.isChangeValid(change)) {
                  delta_score += selector.bestScore(i);
                  dag.addArc(change.node1(), change.node2());
                  impacted_queues[change.node2()] = true;
                  selector.applyChangeWithoutScoreUpdate(change);
                  ++nb_changes_applied;
                }
 
                break;
 
              case GraphChangeType::ARC_DELETION :
                if (!impacted_queues[change.node2()] && selector.isChangeValid(change)) {
                  delta_score += selector.bestScore(i);
                  dag.eraseArc(Arc(change.node1(), change.node2()));
                  impacted_queues[change.node2()] = true;
                  selector.applyChangeWithoutScoreUpdate(change);
                  ++nb_changes_applied;
                }
 
                break;
 
              case GraphChangeType::ARC_REVERSAL :
                if ((!impacted_queues[change.node1()]) && (!impacted_queues[change.node2()])
                    && selector.isChangeValid(change)) {
                  delta_score += selector.bestScore(i);
                  dag.eraseArc(Arc(change.node1(), change.node2()));
                  dag.addArc(change.node2(), change.node1());
                  impacted_queues[change.node1()] = true;
                  impacted_queues[change.node2()] = true;
                  selector.applyChangeWithoutScoreUpdate(change);
                  ++nb_changes_applied;
                }
 
                break;
 
              default :
                GUM_ERROR(OperationNotAllowed,
                          "edge modifications are not supported by local search")
            }
          }
        }
 
        selector.updateScoresAfterAppliedChanges();
 
        // reset the impacted queue and applied changes structures
        for (auto iter = impacted_queues.begin(); iter != impacted_queues.end(); ++iter) {
          *iter = false;
        }
 
        updateApproximationScheme(nb_changes_applied);
 
      } while (nb_changes_applied && continueApproximationScheme(delta_score));
 
      stopApproximationScheme();   // just to be sure of the approximationScheme
                                   // has
                                   // been notified of the end of looop
 
      return dag;
    }

    template < typename GUM_SCALAR, typename GRAPH_CHANGES_SELECTOR, typename PARAM_ESTIMATOR >
    BayesNet< GUM_SCALAR > GreedyHillClimbing::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 */