/****************************************************************************
 *   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    *
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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 A class for parameterizing EM's parameter learning approximations
 *
 * @author Christophe GONZALES(_at_AMU) and Pierre-Henri WUILLEMIN(_at_LIP6)
 */
#ifndef GUM_LEARNING_EM_APPROX_SCHEME_H
#define GUM_LEARNING_EM_APPROX_SCHEME_H

#include <algorithm>
#include <string>
#include <vector>

#include <agrum/agrum.h>

#include <agrum/base/core/approximations/approximationScheme.h>
#include <agrum/base/core/approximations/approximationSchemeListener.h>

namespace gum {

  namespace learning {

    /**
     * @class EMApproximationScheme
     * @brief A class for parameterizing EM's parameter learning approximations
     * @ingroup learning_param_utils
     */
    class EMApproximationScheme: public ApproximationScheme {
      public:
      /**
       * @brief initializes the EM parameter learning approximation with the min rate criterion
       * @param verbosity if set to true, EM will keep track of its history
       */
      explicit EMApproximationScheme(bool verbosity = false);

      ~EMApproximationScheme() override = default;

      /**
       * @brief sets the stopping criterion of EM as being the minimal difference between two
       * consecutive log-likelihoods
       * @param eps the log-likelihood difference below which EM stops its iterations
       * @warning setting this stopping criterion disables the min rate criterion (if it was
       * enabled)
       * @throw OutOfBounds Raised if eps <= 0
       */
      void setEpsilon(double eps) override;

      /**
       * @brief sets the stopping criterion of EM as being the minimal difference between two
       * consecutive log-likelihoods
       * @param eps the log-likelihood difference below which EM stops its iterations
       * @warning setting this stopping criterion disables the min rate criterion (if it was
       * enabled)
       * @throw OutOfBounds Raised if eps <= 0
       */
      void setMinDiffEpsilon(double eps);

      /**
       * @brief sets the stopping criterion of EM as being the minimal log-likelihood's evolution
       * rate
       * @param rate the log-likelihood evolution rate below which EM stops its iterations
       * @warning setting this stopping criterion disables the min diff criterion (if it was
       * enabled)
       * @throw OutOfBounds if rate<0
       */
      void setMinEpsilonRate(double rate) override;
    };

  }   // namespace learning

}   // namespace gum

#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/paramUtils/EMApproximationScheme_inl.h>
#endif

#endif   // GUM_LEARNING_EM_APPROX_SCHEME_H
