/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2023, Individual contributors, see AUTHORS file     |
   | See: https://www.mrpt.org/Authors - All rights reserved.               |
   | Released under BSD License. See: https://www.mrpt.org/License          |
   +------------------------------------------------------------------------+ */
#pragma once

#include <mrpt/bayes/CParticleFilterCapable.h>
#include <mrpt/bayes/CParticleFilterData.h>
#include <mrpt/math/TPose3D.h>
#include <mrpt/obs/CActionRobotMovement2D.h>
#include <mrpt/poses/CPose3D.h>
#include <mrpt/poses/CPose3DPDFGaussian.h>
#include <mrpt/poses/CPoseRandomSampler.h>
#include <mrpt/slam/TKLDParams.h>
#include <mrpt/system/COutputLogger.h>

namespace mrpt::slam
{
// Frwd decl:
template <class PARTICLETYPE, class BINTYPE>
void KLF_loadBinFromParticle(
	BINTYPE& outBin, const TKLDParams& opts,
	const PARTICLETYPE* currentParticleValue = nullptr,
	const mrpt::math::TPose3D* newPoseToBeInserted = nullptr);

/** A set of common data shared by PF implementations for both SLAM and
 * localization
 *   \ingroup mrpt_slam_grp
 */
template <
	class PARTICLE_TYPE, class MYSELF,
	mrpt::bayes::particle_storage_mode STORAGE>
class PF_implementation : public mrpt::system::COutputLogger
{
   public:
	PF_implementation() : mrpt::system::COutputLogger("PF_implementation") {}

   protected:
	/** \name Data members and methods used by generic PF implementations
		@{ */

	mrpt::obs::CActionRobotMovement2D m_accumRobotMovement2D;
	bool m_accumRobotMovement2DIsValid{false};
	mrpt::poses::CPose3DPDFGaussian m_accumRobotMovement3D;
	bool m_accumRobotMovement3DIsValid{false};

	/** Used in al PF implementations. \sa
	 * PF_SLAM_implementation_gatherActionsCheckBothActObs */
	mrpt::poses::CPoseRandomSampler m_movementDrawer;
	/** Auxiliary variable used in the "pfAuxiliaryPFOptimal" algorithm. */
	mutable mrpt::math::CVectorDouble m_pfAuxiliaryPFOptimal_estimatedProb;
	/** Auxiliary variable used in the "pfAuxiliaryPFStandard" algorithm. */
	mutable mrpt::math::CVectorDouble m_pfAuxiliaryPFStandard_estimatedProb;
	/** Auxiliary variable used in the "pfAuxiliaryPFOptimal" algorithm. */
	mutable mrpt::math::CVectorDouble m_pfAuxiliaryPFOptimal_maxLikelihood;
	/** Auxiliary variable used in the "pfAuxiliaryPFOptimal" algorithm. */
	mutable std::vector<mrpt::math::TPose3D>
		m_pfAuxiliaryPFOptimal_maxLikDrawnMovement;
	std::vector<bool> m_pfAuxiliaryPFOptimal_maxLikMovementDrawHasBeenUsed;

	/**  Compute w[i]*p(z_t | mu_t^i), with mu_t^i being
	 *    the mean of the new robot pose
	 *
	 * \param action MUST be a "const CPose3D*"
	 * \param observation MUST be a "const CSensoryFrame*"
	 */
	template <class BINTYPE>  // Template arg. actually not used, just to allow
	// giving the definition in another file later on
	static double PF_SLAM_particlesEvaluator_AuxPFStandard(
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		const mrpt::bayes::CParticleFilterCapable* obj, size_t index,
		const void* action, const void* observation);

	template <class BINTYPE>  // Template arg. actually not used, just to allow
	// giving the definition in another file later on
	static double PF_SLAM_particlesEvaluator_AuxPFOptimal(
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		const mrpt::bayes::CParticleFilterCapable* obj, size_t index,
		const void* action, const void* observation);

	/** @} */

	/** \name The generic PF implementations for localization & SLAM.
		@{ */

	/** A generic implementation of the PF method
	 * "prediction_and_update_pfAuxiliaryPFOptimal" (optimal sampling with
	 * rejection sampling approximation),
	 *  common to both localization and mapping.
	 *
	 * - BINTYPE: TPoseBin or whatever to discretize the sample space for
	 * KLD-sampling.
	 *
	 *  This method implements optimal sampling with a rejection sampling-based
	 * approximation of the true posterior.
	 *  For details, see the papers:
	 *
	 *  J.L. Blanco, J. Gonzalez, and J.-A. Fernandez-Madrigal,
	 *    "An Optimal Filtering Algorithm for Non-Parametric Observation Models
	 * in
	 *     Robot Localization," in Proc. IEEE International Conference on
	 * Robotics
	 *     and Automation (ICRA'08), 2008, pp. 461-466.
	 */
	template <class BINTYPE>
	void PF_SLAM_implementation_pfAuxiliaryPFOptimal(
		const mrpt::obs::CActionCollection* actions,
		const mrpt::obs::CSensoryFrame* sf,
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		const TKLDParams& KLD_options);

	/** A generic implementation of the PF method
	 * "prediction_and_update_pfAuxiliaryPFStandard" (Auxiliary particle filter
	 * with the standard proposal),
	 *  common to both localization and mapping.
	 *
	 * - BINTYPE: TPoseBin or whatever to discretize the sample space for
	 * KLD-sampling.
	 *
	 *  This method is described in the paper:
	 *   Pitt, M.K.; Shephard, N. (1999). "Filtering Via Simulation: Auxiliary
	 * Particle Filters".
	 *    Journal of the American Statistical Association 94 (446): 590-591.
	 * doi:10.2307/2670179.
	 *
	 */
	template <class BINTYPE>
	void PF_SLAM_implementation_pfAuxiliaryPFStandard(
		const mrpt::obs::CActionCollection* actions,
		const mrpt::obs::CSensoryFrame* sf,
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		const TKLDParams& KLD_options);

	/** A generic implementation of the PF method "pfStandardProposal" (standard
	 * proposal distribution, that is, a simple SIS particle filter),
	 *  common to both localization and mapping.
	 *
	 * - BINTYPE: TPoseBin or whatever to discretize the sample space for
	 * KLD-sampling.
	 */
	template <class BINTYPE>
	void PF_SLAM_implementation_pfStandardProposal(
		const mrpt::obs::CActionCollection* actions,
		const mrpt::obs::CSensoryFrame* sf,
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		const TKLDParams& KLD_options);

	/** @} */

   public:
	/** \name Virtual methods that the PF_implementations assume exist.
		@{ */

	/** Return the last robot pose in the i'th particle; is_valid_pose will be
	 * false if there is no such last pose.
	 * \exception std::exception on out-of-range particle index */
	virtual mrpt::math::TPose3D getLastPose(
		size_t i, bool& is_valid_pose) const = 0;

	virtual void PF_SLAM_implementation_custom_update_particle_with_new_pose(
		PARTICLE_TYPE* particleData,
		const mrpt::math::TPose3D& newPose) const = 0;

	/** This is the default algorithm to efficiently replace one old set of
	 * samples by another new set.
	 *  The method uses pointers to make fast copies the first time each
	 * particle is duplicated, then
	 *   makes real copies for the next ones.
	 *
	 *  Note that more efficient specializations might exist for specific
	 * particle data structs.
	 */
	virtual void PF_SLAM_implementation_replaceByNewParticleSet(
		typename mrpt::bayes::CParticleFilterData<
			PARTICLE_TYPE, STORAGE>::CParticleList& old_particles,
		const std::vector<mrpt::math::TPose3D>& newParticles,
		const std::vector<double>& newParticlesWeight,
		const std::vector<size_t>& newParticlesDerivedFromIdx) const
	{
		// ---------------------------------------------------------------------------------
		// Substitute old by new particle set:
		//   Old are in "m_particles"
		//   New are in "newParticles",
		//   "newParticlesWeight","newParticlesDerivedFromIdx"
		// ---------------------------------------------------------------------------------
		const size_t N = newParticles.size();
		std::remove_reference_t<decltype(old_particles)> newParticlesArray(N);
		if constexpr (STORAGE == mrpt::bayes::particle_storage_mode::POINTER)
		{
			// For efficiency, just copy the "CParticleData" from the old
			// particle into the new one, but this can be done only once:
			const auto N_old = old_particles.size();
			std::vector<bool> oldParticleAlreadyCopied(N_old, false);
			std::vector<PARTICLE_TYPE*> oldParticleFirstCopies(N_old, nullptr);

			auto newPartIt = newParticlesArray.begin();
			for (size_t i = 0; newPartIt != newParticlesArray.end();
				 ++newPartIt, ++i)
			{
				// The weight:
				newPartIt->log_w = newParticlesWeight[i];

				// The data (CParticleData):
				PARTICLE_TYPE* newPartData;
				const size_t i_in_old = newParticlesDerivedFromIdx[i];
				if (!oldParticleAlreadyCopied[i_in_old])
				{
					// The first copy of this old particle:
					newPartData = old_particles[i_in_old].d.release();
					oldParticleAlreadyCopied[i_in_old] = true;
					oldParticleFirstCopies[i_in_old] = newPartData;
				}
				else
				{
					// Make a copy:
					ASSERT_(oldParticleFirstCopies[i_in_old]);
					newPartData =
						new PARTICLE_TYPE(*oldParticleFirstCopies[i_in_old]);
				}

				newPartIt->d.reset(newPartData);
			}  // end for "newPartIt"

			// Now add the new robot pose to the paths:
			//  (this MUST be done after the above loop, separately):
			// Update the particle with the new pose: this part is
			// caller-dependant and must be implemented there:
			newPartIt = newParticlesArray.begin();
			for (size_t i = 0; i < N; ++newPartIt, ++i)
				PF_SLAM_implementation_custom_update_particle_with_new_pose(
					newPartIt->d.get(), newParticles[i]);
		}
		else
		{
			// Particles as values:

			auto newPartIt = newParticlesArray.begin();
			for (size_t i = 0; newPartIt != newParticlesArray.end();
				 ++newPartIt, ++i)
			{
				newPartIt->log_w = newParticlesWeight[i];
				const size_t i_in_old = newParticlesDerivedFromIdx[i];
				newPartIt->d = old_particles[i_in_old].d;
			}

			// Now add the new robot pose to the paths:
			newPartIt = newParticlesArray.begin();
			for (size_t i = 0; i < N; ++newPartIt, ++i)
				PF_SLAM_implementation_custom_update_particle_with_new_pose(
					&newPartIt->d, newParticles[i]);
		}
		// Move to "m_particles"
		old_particles = std::move(newParticlesArray);
	}  // end of PF_SLAM_implementation_replaceByNewParticleSet

	virtual bool PF_SLAM_implementation_doWeHaveValidObservations(
		[[maybe_unused]] const typename mrpt::bayes::CParticleFilterData<
			PARTICLE_TYPE, STORAGE>::CParticleList& particles,
		[[maybe_unused]] const mrpt::obs::CSensoryFrame* sf) const
	{
		return true;  // By default, always process the SFs.
	}

	/** Make a specialization if needed, eg. in the first step in SLAM.  */
	virtual bool PF_SLAM_implementation_skipRobotMovement() const
	{
		return false;  // By default, always allow the robot to move!
	}

	/** Evaluate the observation likelihood for one particle at a given location
	 */
	virtual double PF_SLAM_computeObservationLikelihoodForParticle(
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		size_t particleIndexForMap, const mrpt::obs::CSensoryFrame& observation,
		const mrpt::poses::CPose3D& x) const = 0;

	/** @} */

	/** Auxiliary method called by PF implementations: return true if we have
	 * both action & observation,
	 *   otherwise, return false AND accumulate the odometry so when we have an
	 * observation we didn't lose a thing.
	 *   On return=true, the "m_movementDrawer" member is loaded and ready to
	 * draw samples of the increment of pose since last step.
	 *  This method is smart enough to accumulate CActionRobotMovement2D or
	 * CActionRobotMovement3D, whatever comes in.
	 */
	template <class BINTYPE>  // Template arg. actually not used, just to allow
	// giving the definition in another file later on
	bool PF_SLAM_implementation_gatherActionsCheckBothActObs(
		const mrpt::obs::CActionCollection* actions,
		const mrpt::obs::CSensoryFrame* sf);

   private:
	/** The shared implementation body of two PF methods: APF and Optimal-APF,
	 * depending on USE_OPTIMAL_SAMPLING */
	template <class BINTYPE>
	void PF_SLAM_implementation_pfAuxiliaryPFStandardAndOptimal(
		const mrpt::obs::CActionCollection* actions,
		const mrpt::obs::CSensoryFrame* sf,
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		const TKLDParams& KLD_options, const bool USE_OPTIMAL_SAMPLING);

	template <class BINTYPE>
	void PF_SLAM_aux_perform_one_rejection_sampling_step(
		const bool USE_OPTIMAL_SAMPLING, const bool doResample,
		const double maxMeanLik,
		size_t k,  // The particle from the old set "m_particles[]"
		const mrpt::obs::CSensoryFrame* sf,
		const mrpt::bayes::CParticleFilter::TParticleFilterOptions& PF_options,
		mrpt::poses::CPose3D& out_newPose, double& out_newParticleLogWeight);

};	// end PF_implementation
}  // namespace mrpt::slam
