K2_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>
#include <agrum/BN/learning/structureUtils/graphChangesGenerator4K2.h>
 
#include <type_traits>
 
namespace gum {
 
  namespace learning {

    template < typename GRAPH_CHANGES_SELECTOR >
    DAG K2::learnStructure(GRAPH_CHANGES_SELECTOR& selector, DAG initial_dag) {
      // check that we used a selector compatible with the K2 algorithm
      static_assert(std::is_base_of< _GraphChangesGenerator4K2_,
                                     typename GRAPH_CHANGES_SELECTOR::GeneratorType >::value,
                    "K2 must be called with a K2-compliant Graph Change Generator");
 
      // check that the order passed in argument concerns all the nodes
      // __checkOrder(modal);
 
      // get the generator and assign the order
      auto& generator = selector.graphChangeGenerator();
      generator.setOrder(_order_);
 
      // use the greedy hill climbing algorithm to perform the search
      return GreedyHillClimbing::learnStructure(selector, initial_dag);
    }

    template < typename GUM_SCALAR, typename GRAPH_CHANGES_SELECTOR, typename PARAM_ESTIMATOR >
    BayesNet< GUM_SCALAR >
        K2::learnBN(GRAPH_CHANGES_SELECTOR& selector, PARAM_ESTIMATOR& estimator, DAG initial_dag) {
      // check that we used a selector compatible with the K2 algorithm
      static_assert(std::is_base_of< _GraphChangesGenerator4K2_,
                                     typename GRAPH_CHANGES_SELECTOR::GeneratorType >::value,
                    "K2 must be called with a K2-compliant Graph Change Generator");
 
      // check that the order passed in argument concerns all the nodes
      // __checkOrder(modal);
 
      // get the generator and assign the order
      auto& generator = selector.graphChangeGenerator();
      generator.setOrder(_order_);
 
      // use the greedy hill climbing algorithm to perform the search
      return GreedyHillClimbing::learnBN< GUM_SCALAR >(selector, estimator, initial_dag);
    }
 
  } /* namespace learning */
 
} /* namespace gum */