ShaferShenoyLIMIDInference_tpl.h

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#pragma once
 

 
#include <agrum/ID/inference/ShaferShenoyLIMIDInference.h>   // to ease IDE parser
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
// to ease parsing by IDE
#  include <limits>
 
#  include <agrum/base/multidim/tensor.h>
#  include <agrum/ID/inference/ShaferShenoyLIMIDInference.h>
#  include <agrum/ID/inference/tools/decisionTensor.h>
 
#  define GUM_SSLI_TRACE_ON(x)          // GUM_TRACE(x)
#  define GUM_SSLI_TENSOR_TRACE_ON(x)   // GUM_TRACE(x)
 
namespace gum {
 
  template < GUM_Numeric GUM_SCALAR >
  ShaferShenoyLIMIDInference< GUM_SCALAR >::ShaferShenoyLIMIDInference(
      const InfluenceDiagram< GUM_SCALAR >* infDiag) :
      InfluenceDiagramInference< GUM_SCALAR >(infDiag) {
    GUM_CONSTRUCTOR(ShaferShenoyLIMIDInference);
    createReduced_();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  ShaferShenoyLIMIDInference< GUM_SCALAR >::~ShaferShenoyLIMIDInference() {
    GUM_DESTRUCTOR(ShaferShenoyLIMIDInference);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::clear() {
    GraphicalModelInference< GUM_SCALAR >::clear();
    noForgettingOrder_.clear();
    reduced_.clear();
    reducedJunctionTree_.clear();
    solvabilityOrder_.clear();
    posteriors_.clear();
    unconditionalDecisions_.clear();
    strategies_.clear();
    reversePartialOrder_.clear();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::onStateChanged_() {}
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::onEvidenceAdded_(const NodeId id,
                                                                  bool         isHardEvidence) {
    const InfluenceDiagram< GUM_SCALAR >& infdiag = this->influenceDiagram();
    if (infdiag.isUtilityNode(id)) { GUM_ERROR(InvalidNode, "No evidence on a utility node.") }
    if (infdiag.isDecisionNode(id)) {
      if (!isHardEvidence) GUM_ERROR(InvalidNode, "No soft evidence on a decision node.")
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::onEvidenceErased_(const NodeId id,
                                                                   bool         isHardEvidence) {}
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::onAllEvidenceErased_(bool contains_hard_evidence) {
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::onEvidenceChanged_(const NodeId id,
                                                                    bool hasChangedSoftHard) {}
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::onModelChanged_(const GraphicalModel* model) {
    createReduced_();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::updateOutdatedStructure_() {
    createReduced_();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::updateOutdatedTensors_() {}
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::makeInference_() {
    if (!isSolvable()) { GUM_ERROR(FatalError, "This LIMID/Influence Diagram is not solvable.") }
 
    PhiNodeProperty phi;
    PsiArcProperty  psi;
 
    GUM_SSLI_TRACE_ON("\n\n")
 
    initializingInference_(phi, psi);
    // message passing (using reverse order of solvabilityOrder)
    // first collect of phis into root
    const auto firstRootIndice = 0;
    collectingMessage_(phi, psi, node_to_clique_[solvabilityOrder_[firstRootIndice]]);
    deciding_(phi, psi, solvabilityOrder_[firstRootIndice]);
 
    for (Idx nextRootIndice = 1; nextRootIndice < solvabilityOrder_.size(); nextRootIndice++) {
      if (node_to_clique_[solvabilityOrder_[nextRootIndice - 1]]
          != node_to_clique_[solvabilityOrder_[nextRootIndice]]) {
        collectingToFollowingRoot_(phi,
                                   psi,
                                   node_to_clique_[solvabilityOrder_[nextRootIndice - 1]],
                                   node_to_clique_[solvabilityOrder_[nextRootIndice]]);
      }
      deciding_(phi, psi, solvabilityOrder_[nextRootIndice]);
    }
 
    // last distribution
    distributingMessage_(phi,
                         psi,
                         node_to_clique_[solvabilityOrder_[solvabilityOrder_.size() - 1]]);
    computingPosteriors_(phi, psi);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::initializingInference_(PhiNodeProperty& phi,
                                                                        PsiArcProperty&  psi) {
    const auto& jt      = *junctionTree();
    const auto& infdiag = this->influenceDiagram();
    // init JT tensors and separators
 
    for (const auto node: jt.nodes()) {
      phi.insert(node, DecisionTensor< GUM_SCALAR >());
      for (const auto nei: jt.neighbours(node)) {
        psi.insert(Arc(node, nei), DecisionTensor< GUM_SCALAR >());
        if (node < nei) {   // to do it only once by edge
          // we create the set of vars in node and nei  (cached in varsSeparators_)
          for (const auto n: jt.clique(node) * jt.clique(nei))
            varsSeparator_.getWithDefault(Edge(node, nei), SetOfVars())
                .insert(&(infdiag.variable(n)));
        }
      }
    }
    for (const auto node: infdiag.nodes()) {
      const auto clik = node_to_clique_[node];
      if (this->hasEvidence(node)) {
        auto q = *(this->evidence()[node]);
        phi[clik].insertProba(q.normalize());
      }
 
      if (infdiag.isDecisionNode(node)) {
        if (!this->hasEvidence(node)) {
          auto p = (Tensor< GUM_SCALAR >() << infdiag.variable(node)).fillWith(1).normalize();
          phi[clik].insertProba(p);   // WITHOUT NORMALIZATION !!!
        }
      } else if (infdiag.isChanceNode(node)) phi[clik].insertProba(infdiag.cpt(node));
      else if (infdiag.isUtilityNode(node)) phi[clik].insertUtility(infdiag.utility(node));
      else GUM_ERROR(FatalError, "Node " << node << " has no type.")
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::_creatingJunctionTree_() {
    const auto& infdiag = this->influenceDiagram();
    auto        moral   = reduced_.moralGraph();
 
    // once the moral graph is completed, we remove the utility nodes before
    // triangulation
    NodeProperty< Size > modalities;
    for (const auto node: infdiag.nodes())
      if (infdiag.isUtilityNode(node)) {
        moral.eraseNode(node);
      } else {
        modalities.insert(node, infdiag.variable(node).domainSize());
      }
    DefaultTriangulation triangulation(&moral, &modalities);
    reducedJunctionTree_ = triangulation.junctionTree();
    _findingCliqueForEachNode_(triangulation);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::_findingCliqueForEachNode_(
      DefaultTriangulation& triangulation) {
    // indicate, for each node of the moral graph a clique in  _JT_ that can
    // contain its conditional probability table
    const auto& infdiag = this->influenceDiagram();
    NodeId      first_eliminated_node;
    Idx         elim_number;
    node_to_clique_.clear();
    const std::vector< NodeId >& JT_elim_order = triangulation.eliminationOrder();
    NodeProperty< Idx >          elim_order(Size(JT_elim_order.size()));
    for (Idx i = Idx(0), size = JT_elim_order.size(); i < size; ++i)
      elim_order.insert(JT_elim_order[i], (int)i);
    for (const auto node: reduced_.nodes()) {
      if (infdiag.isUtilityNode(node)) {
        // utility nodes are not in the junction tree but we want to associate a
        // clique as well
        first_eliminated_node = node;
        elim_number = std::numeric_limits< NodeId >::max();   // an impossible elim_number;
      } else {
        // get the variables in the tensor of node (and its parents)
        first_eliminated_node = node;
        elim_number           = elim_order[first_eliminated_node];
      }
 
      for (const auto parent: reduced_.parents(node)) {
        if (elim_order[parent] < elim_number) {
          elim_number           = elim_order[parent];
          first_eliminated_node = parent;
        }
      }
 
      // first_eliminated_node contains the first var (node or one of its
      // parents) eliminated => the clique created during its elimination
      // contains node and all of its parents => it can contain the tensor
      // assigned to the node in the BN
      node_to_clique_.insert(node, triangulation.createdJunctionTreeClique(first_eliminated_node));
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  std::pair< GUM_SCALAR, GUM_SCALAR > ShaferShenoyLIMIDInference< GUM_SCALAR >::MEU() {
    if (!this->isInferenceDone()) GUM_ERROR(OperationNotAllowed, "Call MakeInference first")
 
    const InfluenceDiagram< GUM_SCALAR >& infdiag = this->influenceDiagram();
 
    GUM_SCALAR resmean = 0;
    GUM_SCALAR resvar  = 0;
    for (auto node: infdiag.nodes()) {
      if (infdiag.isUtilityNode(node)) {
        auto p = meanVar(node);
        resmean += p.first;
        resvar += p.second;
      }
    }
    return std::pair< GUM_SCALAR, GUM_SCALAR >(resmean, resvar);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  gum::Tensor< GUM_SCALAR >
      ShaferShenoyLIMIDInference< GUM_SCALAR >::optimalDecision(NodeId decisionId) {
    if (!this->isInferenceDone()) GUM_ERROR(OperationNotAllowed, "Call MakeInference first")
 
    const InfluenceDiagram< GUM_SCALAR >& infdiag = this->influenceDiagram();
    if (!infdiag.isDecisionNode(decisionId))
      GUM_ERROR(InvalidNode,
                infdiag.variable(decisionId).name()
                    << "(" << decisionId << ") is not a decision node.")
 
    return strategies_[decisionId];
  }
 
  template < GUM_Numeric GUM_SCALAR >
  bool ShaferShenoyLIMIDInference< GUM_SCALAR >::isSolvable() const {
    return (!solvabilityOrder_.empty());
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::createReduced_() {
    // from LIMIDS of decision Problems, Lauritzen et Nilsson, 1999
    reduced_.clear();
    reducedJunctionTree_.clear();
    solvabilityOrder_.clear();
    reversePartialOrder_.clear();
    posteriors_.clear();
    unconditionalDecisions_.clear();
    strategies_.clear();
    const InfluenceDiagram< GUM_SCALAR >& infdiag = this->influenceDiagram();
 
    NodeSet utilities;
 
    // build reduced_
    for (auto node: infdiag.nodes()) {
      reduced_.addNodeWithId(node);
      if (infdiag.isUtilityNode(node)) { utilities.insert(node); }
    }
 
    for (const auto& arc: infdiag.arcs())
      reduced_.addArc(arc.tail(), arc.head());
 
    _completingNoForgettingAssumption_();
    _creatingPartialOrder_(utilities);
    _checkingSolvability_(utilities);
    if (isSolvable()) {
      _reducingLIMID_();
      _creatingJunctionTree_();
    }
 
    this->setState_(GraphicalModelInference< GUM_SCALAR >::StateOfInference::OutdatedStructure);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::_completingNoForgettingAssumption_() {
    // force no forgetting if necessary
    if (hasNoForgettingAssumption()) {
      auto last = *(noForgettingOrder_.begin());
      for (auto node: noForgettingOrder_)
        if (node == last)   // first one
          continue;
        else {              // we deal with last->node
                            // adding the whole family of last as parents of node
          if (!reduced_.existsArc(last, node)) { reduced_.addArc(last, node); }
          for (auto par: reduced_.parents(last)) {
            if (!reduced_.existsArc(par, node)) reduced_.addArc(par, node);
          }
          last = node;
        }
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::_checkingSolvability_(const NodeSet& utilities) {
    if (hasNoForgettingAssumption()) {
      solvabilityOrder_ = noForgettingOrder_;
      std::reverse(solvabilityOrder_.begin(), solvabilityOrder_.end());
      return;
    }
 
    solvabilityOrder_.clear();
    for (const auto& sen: reversePartialOrder()) {
      NodeSet tobetested = sen;
      while (!tobetested.empty()) {
        bool foundOne = false;
        for (const auto& node: tobetested) {
          const auto us = utilities * reduced_.descendants(node);
          NodeSet    decs;
          for (const auto dec: tobetested)
            if (dec != node) decs += reduced_.family(dec);
          if (reduced_.dSeparation(decs, us, reduced_.family(node))) {
            solvabilityOrder_.push_back(node);
            foundOne = true;
            tobetested.erase(node);
            break;
          }
        }
        if (!foundOne) {   // no solvability
          solvabilityOrder_.clear();
          return;
        }
      }
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::_reducingLIMID_() {
    for (const auto& sen: reversePartialOrder_) {
      for (auto n: sen) {
        for (auto p: nonRequisiteNodes_(n))
          reduced_.eraseArc(Arc(p, n));
      }
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::_creatingPartialOrder_(const NodeSet& utilities) {
    const InfluenceDiagram< GUM_SCALAR >& infdiag = this->influenceDiagram();
    NodeProperty< Size >                  level;
 
    for (const auto& node: utilities)
      level.insert(node, 0);   // utility node is level 0
 
    // creating the partial order
    Size max_level = 0;
    reversePartialOrder_.clear();
    reversePartialOrder_.resize(infdiag.size());
    NodeSet currents;
    for (auto node: infdiag.nodes()) {
      if (infdiag.isUtilityNode(node)) continue;
      if (reduced_.children(node).isSubsetOrEqual(utilities)) {
        currents.clear();
        currents.insert(node);
        level.insert(node, 0);
        while (!currents.empty()) {
          NodeId elt = *(currents.begin());
          currents.erase(elt);
 
          if (infdiag.isDecisionNode(elt)) reversePartialOrder_[level[elt]].insert(elt);
 
          for (auto parent: reduced_.parents(elt)) {
            Size lev = 0;
            Size newl;
            bool ok_to_add = true;
            for (auto child: reduced_.children(parent)) {
              if (!level.exists(child)) {
                ok_to_add = false;
                break;
              }
              newl = level[child];
              if (infdiag.isDecisionNode(child)) newl += 1;
              if (lev < newl) lev = newl;
            }
            if (ok_to_add) {
              currents.insert(parent);
              if (level.exists(parent)) {
                if (level[parent] != lev)
                  GUM_ERROR(FatalError,
                            "Trying to set level[" << parent << "] to level=" << lev
                                                   << " but already is " << level[parent]);
              } else {
                level.insert(parent, lev);
              }
 
              if (max_level < lev) max_level = lev;
            }
          }
        }
      }
    }
    Size levmax = 0;
    for (const auto& k: reversePartialOrder_) {
      if (k.empty()) break;
      levmax++;
    }
    reversePartialOrder_.resize(levmax);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  std::vector< NodeSet > ShaferShenoyLIMIDInference< GUM_SCALAR >::reversePartialOrder() const {
    return reversePartialOrder_;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  bool ShaferShenoyLIMIDInference< GUM_SCALAR >::hasNoForgettingAssumption() const {
    return !noForgettingOrder_.empty();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::addNoForgettingAssumption(
      const std::vector< std::string >& names) {
    addNoForgettingAssumption(this->influenceDiagram().ids(names));
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::addNoForgettingAssumption(
      const std::vector< NodeId >& ids) {
    const auto& infdiag = this->influenceDiagram();
    for (const auto node: ids) {
      if (!infdiag.exists(node)) GUM_ERROR(NotFound, node << " is not a NodeId")
      if (!infdiag.isDecisionNode(node))
        GUM_ERROR(TypeError,
                  "Node " << node << " (" << infdiag.variable(node).name()
                          << ") is not a decision node");
    }
    if (infdiag.decisionNodeSize() != ids.size())
      GUM_ERROR(SizeError, "Some decision nodes are missing in the sequence " << ids)
 
    noForgettingOrder_ = ids;
    createReduced_();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  NodeSet ShaferShenoyLIMIDInference< GUM_SCALAR >::nonRequisiteNodes_(NodeId d) const {
    const InfluenceDiagram< GUM_SCALAR >& infdiag = this->influenceDiagram();
 
    if (!infdiag.isDecisionNode(d)) GUM_ERROR(TypeError, d << " is not a decision node")
 
    NodeSet res;
    if (reduced_.parents(d).empty()) return res;
 
    NodeSet descUs;
    for (const auto n: reduced_.descendants(d))
      if (infdiag.isUtilityNode(n)) descUs.insert(n);
 
    NodeSet cumul{descUs};
    cumul << d;
    auto g = reduced_.moralizedAncestralGraph(cumul);
 
    NodeSet family{reduced_.parents(d)};
    family << d;
    bool notReq;
    for (const auto p: reduced_.parents(d)) {
      notReq = true;
      for (const auto u: descUs) {
        if (g.hasUndirectedPath(p, u, family)) {
          notReq = false;
          break;
        }
      }
      if (notReq) res << p;
    }
    return res;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  InfluenceDiagram< GUM_SCALAR > ShaferShenoyLIMIDInference< GUM_SCALAR >::reducedLIMID() const {
    const auto&                    infdiag = this->influenceDiagram();
    InfluenceDiagram< GUM_SCALAR > res;
    for (auto node: infdiag.nodes()) {
      if (infdiag.isChanceNode(node)) res.addChanceNode(infdiag.variable(node), node);
      else if (infdiag.isDecisionNode(node)) res.addDecisionNode(infdiag.variable(node), node);
      else   // (infdiag.isUtilityNode(node))
        res.addUtilityNode(infdiag.variable(node), node);
    }
 
    for (const auto& arc: reduced_.arcs()) {
      res.addArc(arc.tail(), arc.head());
    }
 
    for (auto node: infdiag.nodes()) {
      if (infdiag.isChanceNode(node)) res.cpt(node).fillWith(infdiag.cpt(node));
      else if (infdiag.isUtilityNode(node)) res.utility(node).fillWith(infdiag.utility(node));
    }
 
    // Tensors !!!
    return res;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  const JunctionTree* ShaferShenoyLIMIDInference< GUM_SCALAR >::junctionTree() const {
    if (!isSolvable()) { GUM_ERROR(FatalError, "This LIMID/Influence Diagram is not solvable.") }
    return &reducedJunctionTree_;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::collectingMessage_(PhiNodeProperty& phi,
                                                                    PsiArcProperty&  psi,
                                                                    const NodeId     rootClique) {
    const auto& jt = *junctionTree();
    GUM_SSLI_TRACE_ON("COLLECTING TO " << rootClique << ":"
                                       << this->influenceDiagram().names(jt.clique(rootClique)))
 
    std::function< void(NodeId, NodeId) > parcours = [&](NodeId node, NodeId toRoot) {
      for (const auto nei: jt.neighbours(node)) {
        if (nei != toRoot) parcours(nei, node);
      }
      transmittingMessage_(phi, psi, node, toRoot);
      GUM_SSLI_TRACE_ON("  " << node << "->" << toRoot << " transmitted")
    };
 
    for (const auto nei: jt.neighbours(rootClique)) {
      parcours(nei, rootClique);
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::collectingToFollowingRoot_(PhiNodeProperty& phi,
                                                                            PsiArcProperty&  psi,
                                                                            NodeId fromClique,
                                                                            NodeId toClique) {
    GUM_SSLI_TRACE_ON("COLLECTING FROM ROOT " << fromClique << " TO FOLLOWING ROOT " << toClique)
    const auto& jt = *junctionTree();
 
    std::function< bool(NodeId, NodeId, NodeId) > revparcours
        = [&](NodeId node, NodeId from, NodeId target) {
            if (node == target) return true;
 
            bool   found = false;
            NodeId prec;
            for (const auto nei: jt.neighbours(node)) {
              if (nei != from)
                if (revparcours(nei, node, target)) {
                  prec  = nei;
                  found = true;
                  break;
                }
            }
            if (found) { transmittingMessage_(phi, psi, prec, node); }
            return found;
          };
 
    revparcours(toClique, std::numeric_limits< NodeId >::max(), fromClique);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::deciding_(PhiNodeProperty& phi,
                                                           PsiArcProperty&  psi,
                                                           NodeId           decisionNode) {
    const auto& infdiag = this->influenceDiagram();
    GUM_SSLI_TRACE_ON("DECIDING for " << infdiag.variable(decisionNode).name())
 
    auto& decision = strategies_.getWithDefault(decisionNode, Tensor< GUM_SCALAR >());
 
    if (this->hasHardEvidence(decisionNode)) {
      decision = *(this->evidence()[decisionNode]);
    } else {
      DecisionTensor< double > dp;
      dp = integrating_(phi, psi, node_to_clique_[decisionNode]);
      GUM_SSLI_TENSOR_TRACE_ON(dp)
 
      SetOfVars sev;
      sev.insert(&infdiag.variable(decisionNode));
      for (const auto parent: reduced_.parents(decisionNode)) {
        sev.insert(&infdiag.variable(parent));
      }
      dp = dp ^ sev;
      GUM_SSLI_TENSOR_TRACE_ON(dp)
 
      // SPECIAL CASE FOR DETERMINISTIC DECISION
      sev.erase(&infdiag.variable(decisionNode));   // only the parents in sev
      if (sev.size() == 0) {                        // deterministic decision node
        unconditionalDecisions_.set(decisionNode, dp);
      } else if (dp.probPot.sumIn(sev).normalize().max()
                 == 1) {                            // with deterministic posterior probability
        // we can use marginalization because we know that dp is deterministic
        unconditionalDecisions_.set(
            decisionNode,
            DecisionTensor< double >(dp.probPot.sumOut(sev), dp.utilPot.sumOut(sev)));
      }
      decision = dp.utilPot.putFirst(&infdiag.variable(decisionNode));
 
      binarizingMax_(decision, dp.probPot);
      GUM_SSLI_TENSOR_TRACE_ON(decision)
    }
    GUM_SSLI_TENSOR_TRACE_ON(phi[node_to_clique_[decisionNode]])
    phi[node_to_clique_[decisionNode]].insertProba(decision);
    GUM_SSLI_TENSOR_TRACE_ON(phi[node_to_clique_[decisionNode]])
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::binarizingMax_(
      const Tensor< GUM_SCALAR >& decision,
      const Tensor< GUM_SCALAR >& proba) const {   // compute the decisions (as maxEU)
    Instantiation I(decision);
    const auto&   firstvar = decision.variable(0);
    for (I.setFirst(); !I.end(); I.incNotVar(firstvar)) {
      I.setFirstVar(firstvar);
      while (proba[I] == 0) {
        I.incVar(firstvar);
        if (I.end()) {   // for non valid proba, we keep the first value (by
                         // default)²
          I.setFirstVar(firstvar);
          break;
        }
      }
      // we found a non null value of proba
      Idx        argm = I.val(firstvar);
      GUM_SCALAR umax = decision[I];
      GUM_SCALAR pmax = proba[I];
      for (I.incVar(firstvar); !I.end(); I.incVar(firstvar)) {
        // lexicographical order on (u,p)
        if (proba[I] > 0) {
          if ((umax < decision[I]) || ((umax == decision[I]) && (pmax < proba[I]))) {
            umax = decision[I];
            pmax = proba[I];
            argm = I.val(firstvar);
          }
        }
      }
      for (I.setFirstVar(firstvar); !I.end(); I.incVar(firstvar))
        decision.set(I, 0);
      I.chgVal(firstvar, argm);
      decision.set(I, 1);
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::distributingMessage_(PhiNodeProperty& phi,
                                                                      PsiArcProperty&  psi,
                                                                      NodeId           rootClique) {
    const auto& jt = *junctionTree();
    GUM_SSLI_TRACE_ON("DISTRIBUTING FROM " << rootClique << ":"
                                           << this->influenceDiagram().names(jt.clique(rootClique)))
 
    std::function< void(NodeId, NodeId) > parcours = [&](NodeId node, NodeId from) {
      transmittingFinalMessage_(phi, psi, from, node);
      auto res = integrating_(phi, psi, node);
 
      res.probPot
          = gum::DecisionTensor< GUM_SCALAR >::divideEvenZero(res.probPot,
                                                              psi[gum::Arc(node, from)].probPot);
      res.utilPot = res.utilPot - psi[gum::Arc(node, from)].utilPot;
 
      phi.set(node, res);
      GUM_SSLI_TRACE_ON("    -> phi[" << node << "] updated")
      for (const auto nei: jt.neighbours(node)) {
        if (nei != from) parcours(nei, node);
      }
    };
 
    phi.set(rootClique, integrating_(phi, psi, rootClique));
    GUM_SSLI_TRACE_ON("    -> phi[" << rootClique << "] updated")
    GUM_SSLI_TENSOR_TRACE_ON(phi[rootClique])
 
    for (const auto nei: jt.neighbours(rootClique)) {
      parcours(nei, rootClique);
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::transmittingFinalMessage_(PhiNodeProperty& phi,
                                                                           PsiArcProperty&  psi,
                                                                           NodeId fromClique,
                                                                           NodeId toClique) {
    GUM_SSLI_TRACE_ON(fromClique << "->" << toClique << " [final]")
    // no need to integrate phi : already done
    psi.set(Arc(fromClique, toClique),
            phi[fromClique] ^ varsSeparator_[Edge(fromClique, toClique)]);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::transmittingMessage_(PhiNodeProperty& phi,
                                                                      PsiArcProperty&  psi,
                                                                      NodeId           fromClique,
                                                                      NodeId           toClique) {
    GUM_SSLI_TRACE_ON(fromClique << "->" << toClique)
    psi.set(Arc(fromClique, toClique),
            integrating_(phi, psi, fromClique, toClique)
                ^ varsSeparator_[Edge(fromClique, toClique)]);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  DecisionTensor< double >
      ShaferShenoyLIMIDInference< GUM_SCALAR >::integrating_(const PhiNodeProperty& phi,
                                                             const PsiArcProperty&  psi,
                                                             NodeId                 inClique,
                                                             NodeId                 except) const {
    const auto& jt = *junctionTree();
    GUM_SSLI_TRACE_ON("  integrating (except from "
                      << except << ") in " << inClique << ":"
                      << this->influenceDiagram().names(jt.clique(inClique)))
    DecisionTensor< double > res = phi[inClique];
    for (const auto nei: jt.neighbours(inClique))
      if (nei != except) res *= psi[Arc(nei, inClique)];
 
    return res;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  DecisionTensor< double >
      ShaferShenoyLIMIDInference< GUM_SCALAR >::integrating_(const PhiNodeProperty& phi,
                                                             const PsiArcProperty&  psi,
                                                             NodeId inClique) const {
    const auto& jt = *junctionTree();
    GUM_SSLI_TRACE_ON("  integrating in " << inClique << ":"
                                          << this->influenceDiagram().names(jt.clique(inClique)))
    DecisionTensor< double > res = phi[inClique];
 
    for (const auto nei: jt.neighbours(inClique))
      res *= psi[Arc(nei, inClique)];
 
    return res;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void ShaferShenoyLIMIDInference< GUM_SCALAR >::computingPosteriors_(const PhiNodeProperty& phi,
                                                                      const PsiArcProperty&  psi) {
    NodeProperty< DecisionTensor< double > > finalphis;
 
    const auto& infdiag = this->influenceDiagram();
    posteriors_.clear();
    strategies_.clear();
    for (const auto node: infdiag.nodes()) {
      const auto clik = node_to_clique_[node];
      // if (!finalphis.exists(clik)) finalphis.insert(clik, integrating_(phi, psi, clik));
      // const auto& finalphi = finalphis[clik];
      const auto& finalphi = phi[clik];
      GUM_SSLI_TRACE_ON("posterior for " << infdiag.variable(node).name())
 
      DecisionTensor< GUM_SCALAR > res;
 
      if (infdiag.isChanceNode(node)) {
        SetOfVars sev;
        sev.insert(&infdiag.variable(node));
        res          = finalphi ^ sev;
        const auto f = res.probPot.sum();
        res.probPot.scale(1 / f);
      } else if (infdiag.isDecisionNode(node)) {
        SetOfVars sev;
        sev.insert(&infdiag.variable(node));
        SetOfVars family = sev;
        for (const auto par: reduced_.parents(node)) {
          if (infdiag.isChanceNode(par)) family.insert(&infdiag.variable(par));
        }
        // GCC 16 false positive: deep inlining confuses heap-allocated internals
        // of DecisionTensor with the stack object bounds
#  pragma GCC diagnostic push
#  pragma GCC diagnostic ignored "-Warray-bounds"
        const auto dp = finalphi ^ family;
 
        gum::Tensor< double > decision = dp.utilPot.putFirst(&infdiag.variable(node));
        binarizingMax_(decision, dp.probPot);
        strategies_.insert(node, decision);
        res = dp ^ sev;
#  pragma GCC diagnostic pop
        res.probPot.normalize();
        if (unconditionalDecisions_.exists(node)) {
          res.utilPot = unconditionalDecisions_[node].utilPot;
        }
      } else {   // utility
        SetOfVars family;
 
        family.insert(&infdiag.variable(node));
        for (const auto par: reduced_.parents(node)) {
          family.insert(&infdiag.variable(par));
        }
        res = finalphi ^ family;
 
        res.probPot.normalize();
        res.utilPot = infdiag.utility(node);
      }
 
      posteriors_.set(node, res);
    }
  }
 
  template < GUM_Numeric GUM_SCALAR >
  const Tensor< GUM_SCALAR >& ShaferShenoyLIMIDInference< GUM_SCALAR >::posterior(NodeId node) {
    return posteriors_[node].probPot;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  const Tensor< GUM_SCALAR >&
      ShaferShenoyLIMIDInference< GUM_SCALAR >::posteriorUtility(NodeId node) {
    return posteriors_[node].utilPot;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  std::pair< GUM_SCALAR, GUM_SCALAR >
      ShaferShenoyLIMIDInference< GUM_SCALAR >::meanVar(NodeId node) {
    return posteriors_[node].meanVar();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  DAG ShaferShenoyLIMIDInference< GUM_SCALAR >::reducedGraph() const {
    return reduced_;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  gum::Tensor< GUM_SCALAR >
      ShaferShenoyLIMIDInference< GUM_SCALAR >::optimalDecision(std::string_view decisionName) {
    return optimalDecision(this->influenceDiagram().idFromName(decisionName));
  }
 
  template < GUM_Numeric GUM_SCALAR >
  const Tensor< GUM_SCALAR >&
      ShaferShenoyLIMIDInference< GUM_SCALAR >::posterior(std::string_view name) {
    return posterior(this->influenceDiagram().idFromName(name));
  }
 
  template < GUM_Numeric GUM_SCALAR >
  const Tensor< GUM_SCALAR >&
      ShaferShenoyLIMIDInference< GUM_SCALAR >::posteriorUtility(std::string_view name) {
    return posteriorUtility(this->influenceDiagram().idFromName(name));
  }
 
  template < GUM_Numeric GUM_SCALAR >
  std::pair< GUM_SCALAR, GUM_SCALAR >
      ShaferShenoyLIMIDInference< GUM_SCALAR >::meanVar(std::string_view name) {
    return meanVar(this->influenceDiagram().idFromName(name));
  }
 
} /* namespace gum */
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */