KTBNInference.h

Go to the documentation of this file.

/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *                                                                          *
 *   The aGrUM/pyAgrum library is free software; you can redistribute it    *
 *   and/or modify it under the terms of either :                           *
 *                                                                          *
 *    - the GNU Lesser General Public License as published by               *
 *      the Free Software Foundation, either version 3 of the License,      *
 *      or (at your option) any later version,                              *
 *    - the MIT license (MIT),                                              *
 *    - or both in dual license, as here.                                   *
 *                                                                          *
 *   (see https://agrum.gitlab.io/articles/dual-licenses-lgplv3mit.html)    *
 *                                                                          *
 *   This aGrUM/pyAgrum library is distributed in the hope that it will be  *
 *   useful, but WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,          *
 *   INCLUDING BUT NOT LIMITED TO THE WARRANTIES MERCHANTABILITY or FITNESS *
 *   FOR A PARTICULAR PURPOSE  AND NONINFRINGEMENT. IN NO EVENT SHALL THE   *
 *   AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER *
 *   LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,        *
 *   ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR  *
 *   OTHER DEALINGS IN THE SOFTWARE.                                        *
 *                                                                          *
 *   See LICENCES for more details.                                         *
 *                                                                          *
 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *   SPDX-License-Identifier: LGPL-3.0-or-later OR MIT                      *
 *                                                                          *
 *   Contact  : info_at_agrum_dot_org                                       *
 *   homepage : http://agrum.gitlab.io                                      *
 *   gitlab   : https://gitlab.com/agrumery/agrum                           *
 *                                                                          *
 ****************************************************************************/
 

#ifndef GUM_KTBN_INFERENCE_H
#define GUM_KTBN_INFERENCE_H
 
#include <map>
#include <memory>
#include <set>
#include <string>
#include <variant>
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/base/graphs/algorithms/triangulations/defaultTriangulation.h>
#include <agrum/base/variables/discreteVariable.h>
#include <agrum/KTBN/KTBN.h>
 
#include <unordered_map>
#include <unordered_set>
 
namespace gum {

  template < GUM_Numeric GUM_SCALAR >
  class KTBNInference {
    public:
    static constexpr int ATEMPORAL = KTBN< GUM_SCALAR >::ATEMPORAL;
 
    // ===========================================================================
    // ===========================================================================

    explicit KTBNInference(const KTBN< GUM_SCALAR >* ktbn);

    ~KTBNInference() = default;

    KTBNInference(const KTBNInference< GUM_SCALAR >&)                          = delete;
    KTBNInference< GUM_SCALAR >& operator=(const KTBNInference< GUM_SCALAR >&) = delete;


    using NodeKey = std::variant< std::string, std::pair< std::string, int > >;
 
    // ===========================================================================
    // ===========================================================================

    void addIntervention(std::string_view base, int slice, const KTBNModality& value);
    void addIntervention(std::string_view node_name, const KTBNModality& value);

    void addIntervention(const std::vector< std::pair< NodeKey, KTBNModality > >& interventions);

    void eraseIntervention(std::string_view base, int slice);
    void eraseIntervention(std::string_view node_name);
    void clearInterventions();

    bool hasIntervention(std::string_view base, int slice) const;
    bool hasIntervention(std::string_view node_name) const;

    // ===========================================================================
    // ===========================================================================

    void addObservation(std::string_view base, int slice, const KTBNModality& value);
    void addObservation(std::string_view node_name, const KTBNModality& value);

    void addObservation(std::string_view                 base,
                        int                              slice,
                        const std::vector< GUM_SCALAR >& likelihood);
    void addObservation(std::string_view node_name, const std::vector< GUM_SCALAR >& likelihood);

    void addObservations(const std::vector< std::pair< NodeKey, KTBNModality > >& observations);

    void eraseObservation(std::string_view base, int slice);
    void eraseObservation(std::string_view node_name);
    void clearObservation();

    bool hasObservation(std::string_view base, int slice) const;
    bool hasObservation(std::string_view node_name) const;
    bool hasObservation() const;

    // ===========================================================================
    // ===========================================================================

    void addTarget(std::string_view base);

    void eraseTarget(std::string_view base);
    void clearTargets();

    bool isTarget(std::string_view base) const;

    bool isInTargetMode() const;

    // ===========================================================================
    // ===========================================================================

    void makeInference(Size nbTimeSlices);

    const Tensor< GUM_SCALAR >& posterior(std::string_view base, int slice);
    const Tensor< GUM_SCALAR >& posterior(std::string_view node_name);

    const std::vector< Tensor< GUM_SCALAR > >& posteriors(std::string_view base);

    GUM_SCALAR logObservationProbability();

    GUM_SCALAR observationProbability();

    // ===========================================================================
    // ===========================================================================

    const KTBN< GUM_SCALAR >& ktbn() const;

    std::string toString() const;

    const JunctionTree& windowJunctionTree() const;

    Size interfaceSize() const;

 
    private:
    struct _Series_ {
      std::vector< std::unique_ptr< DiscreteVariable > > vars;
      std::vector< Tensor< GUM_SCALAR > >                tensors;
    };

    struct _Slot_ {
      int  base;
      int  lag;
      bool operator==(const _Slot_& o) const;
    };

    struct _Window_ {
      std::vector< _Slot_ > slotOfNode;
      JunctionTree jt;
      NodeId rootC{0};
      NodeId rootD{0};
      std::vector< NodeId >                bfs;
      std::unordered_map< NodeId, NodeId > parentOf;
      std::unordered_map< NodeId, std::vector< int > > factorsOf;
      std::unordered_map< int, NodeId > selfClique;
      std::vector< _Slot_ > Iprev, Icur;
    };

    const KTBN< GUM_SCALAR >* _ktbn_;

    int _k_;

    std::map< std::string, Idx > _interventions_;

    std::map< std::string, std::vector< GUM_SCALAR > > _observations_;

    std::set< std::string > _targets_;

    bool _targeted_mode_{false};

    Size _horizon_{0};

    bool _done_{false};

    GUM_SCALAR _logObservation_{0};

    std::unordered_map< std::string, _Series_ > _posteriors_;

    std::vector< std::string > _temporalSorted_;
    std::vector< std::string > _atemporalSorted_;

    std::vector< std::string > _baseNames_;
    std::size_t                _nbTemporal_{0};
    std::unordered_map< std::string, int > _baseIdx_;

    std::vector< int > _maxLag_;

    std::vector< _Window_ > _windows_;

    std::vector< bool > _requisite_;

    mutable std::vector< std::unordered_map< NodeId, Tensor< GUM_SCALAR > > > _psiCache_;
    mutable std::vector< bool >                                               _psiCached_;

    mutable std::unordered_set< int > _observationSlices_;

    mutable std::unordered_set< int > _interventionSlices_;

    mutable std::unordered_map< NodeId, Tensor< GUM_SCALAR > > _psiScratch_;

    Size _psiKey_(int t) const;

    mutable std::map< std::pair< std::string, int >, Tensor< GUM_SCALAR > > _kernelCache_;


    bool _isTemporal_(const std::string& base) const;
    bool _isAtemporal_(const std::string& base) const;

    static std::string _encode_(const std::string& base, int slice);

    std::pair< std::string, int > _determineNode_(const std::string& name) const;

    void _validateNode_(const std::string& base, int slice) const;

    const DiscreteVariable& _templateVar_(const std::string& base, int slice) const;

    const DiscreteVariable* _varOfSlot_(const _Slot_& s, int t) const;

    const Tensor< GUM_SCALAR >& _buildKernel_(const std::string& p, int t) const;

    std::vector< _Slot_ > _familySlots_(int baseIdx, int t) const;

    std::vector< _Slot_ > _interfaceAfter_(int t) const;

    int _lastConsumerSlice_(int baseIdx, int s) const;

    void _buildWindows_();

    _Window_ _compileWindow_(const std::vector< _Slot_ >& Iprev,
                             const std::vector< _Slot_ >& Icur,
                             int                          t,
                             bool                         withAtemporalFamilies) const;

    void _markRequisite_();


    const _Window_& _windowAt_(int t) const;

    const std::unordered_map< NodeId, Tensor< GUM_SCALAR > >& _windowPotentials_(const _Window_& w,
                                                                                 int t) const;

    void _applyTemporalObservations_(const _Window_&                                     w,
                                     int                                                 t,
                                     std::unordered_map< NodeId, Tensor< GUM_SCALAR > >& psi) const;

    void _fillWindow_(const _Window_&                                     w,
                      int                                                 t,
                      std::unordered_map< NodeId, Tensor< GUM_SCALAR > >& psi,
                      bool withTemporalEvidence = true) const;

    void _propagate_(const _Window_&                                                w,
                     int                                                            t,
                     const std::unordered_map< NodeId, Tensor< GUM_SCALAR > >&      psi,
                     const Tensor< GUM_SCALAR >*                                    inPrev,
                     const Tensor< GUM_SCALAR >*                                    inNext,
                     bool                                                           distribute,
                     std::map< std::pair< NodeId, NodeId >, Tensor< GUM_SCALAR > >& msgs) const;

    Tensor< GUM_SCALAR >
        _belief_(const _Window_&                                                      w,
                 const std::unordered_map< NodeId, Tensor< GUM_SCALAR > >&            psi,
                 const std::map< std::pair< NodeId, NodeId >, Tensor< GUM_SCALAR > >& msgs,
                 const Tensor< GUM_SCALAR >*                                          inPrev,
                 const Tensor< GUM_SCALAR >*                                          inNext,
                 NodeId                                                               c,
                 NodeId skipNeighbour) const;

    void _snapshot_(const std::string& base, int slice, const Tensor< GUM_SCALAR >& marginal);

    const _Series_& _series_(const std::string& base);

  };
 
#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
  extern template class GUM_PUBLIC_KTBN KTBNInference< double >;
#endif
 
}   // namespace gum
 
#include <agrum/KTBN/inference/KTBNInference_tpl.h>
 
#endif /* GUM_KTBN_INFERENCE_H */