/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *   See LICENCES for more details.                                         *
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 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *   SPDX-License-Identifier: LGPL-3.0-or-later OR MIT                      *
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 *   Contact  : info_at_agrum_dot_org                                       *
 *   homepage : http://agrum.gitlab.io                                      *
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 ****************************************************************************/


/**
 * @file
 * @brief This file contains the abstract class definition for computing
 * the probability of evidence entered in Bayesian networks
 *
 * @author Christophe GONZALES(_at_AMU) and Pierre-Henri WUILLEMIN(_at_LIP6)
 */

#ifndef GUM_BAYES_NET_EVIDENCE_INFERENCE_H
#define GUM_BAYES_NET_EVIDENCE_INFERENCE_H

#include <agrum/agrum.h>

#include <agrum/BN/inference/tools/BayesNetInference.h>

namespace gum {


  /**
   * @class EvidenceInference evidenceInference.h
   * <agrum/BN/inference/evidenceInference.h>
   * @brief A generic class for the computation of the probability of
   * evidence entered in Bayesian networks
   * @ingroup bn_group
   *
   * The goal of this class is to provide an interface for the computation
   * of the probability of evidence entered in Bayesian networks
   */
  template < GUM_Numeric GUM_SCALAR >
  class EvidenceInference: public virtual BayesNetInference< GUM_SCALAR > {
    public:
    // ############################################################################
    /// @name Constructors / Destructors
    // ############################################################################
    /// @{

    /// default constructor
    /** @warning note that, by aGrUM's rule, the BN is not copied but only
     * referenced by the inference algorithm. */
    explicit EvidenceInference(const IBayesNet< GUM_SCALAR >* bn);

    /// destructor
    ~EvidenceInference() override;

    /// @}


    // ############################################################################
    /// @name Probability computations
    // ############################################################################
    /// @{

    /// returns the probability P(e) of the evidence enterred into the BN
    virtual GUM_SCALAR evidenceProbability() = 0;

    /// @}
  };


}   // namespace gum

#include <agrum/BN/inference/tools/evidenceInference_tpl.h>


#endif   // GUM_BAYES_NET_EVIDENCE_INFERENCE_H
