simpleCPTDisturber_tpl.h

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

#include <agrum/BN/generator/simpleCPTDisturber.h>
 
namespace gum {
 
  // Default constructor.
  template < GUM_Numeric GUM_SCALAR >
  SimpleCPTDisturber< GUM_SCALAR >::SimpleCPTDisturber() : ICPTDisturber< GUM_SCALAR >() {
    GUM_CONSTRUCTOR(SimpleCPTDisturber);
  }
 
  // Destructor.
  template < GUM_Numeric GUM_SCALAR >
  SimpleCPTDisturber< GUM_SCALAR >::~SimpleCPTDisturber() {
    GUM_DESTRUCTOR(SimpleCPTDisturber);
  }
 
  // Generates a CPT using GUM_SCALAR.
  // @param varID The variable id of the CPT owner.
  // @param cpt A reference on the CPT to fill.
 
  template < GUM_Numeric GUM_SCALAR >
  void SimpleCPTDisturber< GUM_SCALAR >::disturbReducCPT(NodeId                  node,
                                                         BayesNet< GUM_SCALAR >& bayesNet,
                                                         Tensor< GUM_SCALAR >&   src,
                                                         Tensor< GUM_SCALAR >&   marg) {
    Instantiation i_src(src);
    Instantiation i_dest(bayesNet.cpt(node));
    Instantiation i_marg(marg);
 
    for (i_dest.setFirst(); !i_dest.end(); ++i_dest) {
      GUM_SCALAR potval = 0;
 
      i_src.setVals(i_dest);
      for (i_marg.setFirst(); !i_marg.end(); ++i_marg) {
        i_src.setVals(i_marg);
        potval += src.get(i_src) * marg.get(i_marg);
      }
 
      bayesNet.cpt(node).set(i_dest, potval);
    }
 
    bayesNet.cpt(node).normalizeAsCPT();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void SimpleCPTDisturber< GUM_SCALAR >::disturbAugmCPT(NodeId                  node,
                                                        BayesNet< GUM_SCALAR >& bayesNet,
                                                        Tensor< GUM_SCALAR >&   src,
                                                        GUM_SCALAR              variation) {
    Instantiation i(bayesNet.cpt(node));
 
    for (i.setFirst(); !i.end(); ++i)
      bayesNet.cpt(node).set(i, src.get(i) + GUM_SCALAR(randomProba()) * variation);
 
    bayesNet.cpt(node).normalizeAsCPT();
  }
 
} /* namespace gum */