/**
 * Copyright or © or Copr. IETR/INSA - Rennes (2019 - 2025) :
 *
 * Karol Desnos <kdesnos@insa-rennes.fr> (2019 - 2020)
 * Quentin Vacher <qvacher@insa-rennes.fr> (2025)
 *
 * GEGELATI is an open-source reinforcement learning framework for training
 * artificial intelligence based on Tangled Program Graphs (TPGs).
 *
 * This software is governed by the CeCILL-C license under French law and
 * abiding by the rules of distribution of free software. You can use,
 * modify and/ or redistribute the software under the terms of the CeCILL-C
 * license as circulated by CEA, CNRS and INRIA at the following URL
 * "http://www.cecill.info".
 *
 * As a counterpart to the access to the source code and rights to copy,
 * modify and redistribute granted by the license, users are provided only
 * with a limited warranty and the software's author, the holder of the
 * economic rights, and the successive licensors have only limited
 * liability.
 *
 * In this respect, the user's attention is drawn to the risks associated
 * with loading, using, modifying and/or developing or reproducing the
 * software by the user in light of its specific status of free software,
 * that may mean that it is complicated to manipulate, and that also
 * therefore means that it is reserved for developers and experienced
 * professionals having in-depth computer knowledge. Users are therefore
 * encouraged to load and test the software's suitability as regards their
 * requirements in conditions enabling the security of their systems and/or
 * data to be ensured and, more generally, to use and operate it in the
 * same conditions as regards security.
 *
 * The fact that you are presently reading this means that you have had
 * knowledge of the CeCILL-C license and that you accept its terms.
 */

#ifndef EVALUATION_RESULT_H
#define EVALUATION_RESULT_H

#include <cmath>
#include <memory>

#include "selector/selectionMetrics.h"
namespace Learn {
    /**
     * \brief Base class for storing all result of a policy evaluation within a
     * LearningEnvironment.
     *
     * To enable generic learning with the default LearningAgent, all policy
     * evaluation must be convertible to a simple double value. For more complex
     * LearningAgent behavior, like for classification purposes, specific child
     * class can be created.
     */
    class EvaluationResult
    {
      protected:
        /**
         * Selection Metrics register in the evaluation results.
         */
        std::shared_ptr<Selector::SelectionMetrics> selectionMetrics;

        /// Number of evaluation leading to this result.
        size_t nbEvaluation;

      public:
        /**
         * \brief Deleted default constructor.
         */
        EvaluationResult() = delete;

        /**
         * \brief Virtual destructor for polymorphism.
         */
        virtual ~EvaluationResult() = default;

        /**
         * \brief Construct a result from a simple double value.
         *
         * \param[in] selectionMetrics metrics used for the selection.
         * \param[in] nbEval Integer value representing the number of
         * evaluation leading to the recorded score.
         */
        EvaluationResult(
            std::shared_ptr<Selector::SelectionMetrics> selectionMetrics,
            const size_t& nbEval)
            : selectionMetrics{selectionMetrics}, nbEvaluation{nbEval} {};

        /**
         * \brief Virtual method to get the default double equivalent of
         * the reward of the EvaluationResult.
         */
        virtual std::shared_ptr<Selector::SelectionMetrics>
        getSelectionMetrics() const;

        /**
         * \brief Virtual method to get the default number of evaluation of
         * the EvaluationResult.
         */
        virtual size_t getNbEvaluation() const;

        /**
         * \brief Polymorphic addition assignement operator for
         * EvaluationResult.
         *
         * \throw std::runtime_error in case the other EvaluationResult and
         * this have a different typeid.
         */
        virtual EvaluationResult& operator+=(const EvaluationResult& other);
    };

    /**
     * \brief Comparison function to enable sorting of EvaluationResult with
     * STL.
     */
    bool operator<(const EvaluationResult& a, const EvaluationResult& b);
} // namespace Learn

namespace std {
    /// Struct specialization for EvaluationResult
    template <> struct less<std::shared_ptr<Learn::EvaluationResult>>
    {
        /// Comparison operator for sorted containers.
        bool operator()(const std::shared_ptr<Learn::EvaluationResult>& a,
                        const std::shared_ptr<Learn::EvaluationResult>& b) const
        {
            return *a < *b;
        }
    };
}; // namespace std

#endif
