text: /* +------------------------------------------------------------------------+
     |                     Mobile Robot Programming Toolkit (MRPT)            |
     |                          https://www.mrpt.org/                         |
     |                                                                        |
     | Copyright (c) 2005-2019, 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/poses/CPose3DPDF.h>
  
  namespace mrpt::poses
  {
  /** Declares a class that represents a Probability Density function (PDF) of a
   * 3D pose
   *
   *  This class is also the base for the implementation of Monte-Carlo
   * Localization (MCL), in mrpt::slam::CMonteCarloLocalization2D.
   *
   *  See the application "app/pf-localization" for an example of usage.
   *
   * \sa CPose3D, CPose3DPDF, CPoseGaussianPDF
   * \ingroup poses_pdf_grp
   */
  class CPose3DPDFParticles
  	: public CPose3DPDF,
  	  public mrpt::bayes::CParticleFilterData<
  		  mrpt::math::TPose3D, mrpt::bayes::particle_storage_mode::VALUE>,
  	  public mrpt::bayes::CParticleFilterDataImpl<
  		  CPose3DPDFParticles,
  		  mrpt::bayes::CParticleFilterData<
  			  mrpt::math::TPose3D,
  			  mrpt::bayes::particle_storage_mode::VALUE>::CParticleList>
  {
  	DEFINE_SERIALIZABLE(CPose3DPDFParticles)
  
     public:
  	/** Constructor
  	 * \param M The number of m_particles.
  	 */
  	CPose3DPDFParticles(size_t M = 1);
  
  	/** Copy operator, translating if necesary (for example, between m_particles
  	 * and gaussian representations) */
  	void copyFrom(const CPose3DPDF& o) override;
  
  	/** Reset the PDF to a single point: All m_particles will be set exactly to
  	 * the supplied pose.
  	 * \param location The location to set all the m_particles.
  	 * \param particlesCount If this is set to 0 the number of m_particles
  	 * remains unchanged.
  	 *  \sa resetUniform */
  	void resetDeterministic(
  		const mrpt::math::TPose3D& location, size_t particlesCount = 0);
  
  	/** Reset the PDF to an uniformly distributed one, inside of the defined
  	 * "cube".
  	 *
  	 * \param particlesCount New particle count, or leave count unchanged if set
  	 * to -1 (default).
  	 *
  	 * \note Orientations can be outside of the [-pi,pi] range if so desired,
  	 *       but it must hold `phi_max>=phi_min`.
  	 * \sa resetDeterministic
  	 * resetAroundSetOfPoses
  	 */
  	void resetUniform(
  		const mrpt::math::TPose3D& corner_min,
  		const mrpt::math::TPose3D& corner_max, const int particlesCount = -1);
  
  	/** Returns an estimate of the pose, (the mean, or mathematical expectation
  	 * of the PDF), computed as a weighted average over all m_particles. \sa
  	 * getCovariance */
  	void getMean(CPose3D& mean_pose) const override;
  
  	/** Returns an estimate of the pose covariance matrix (6x6 cov matrix) and
  	 * the mean, both at once. \sa getMean */
  	std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const override;
  
  	/** Returns the pose of the i'th particle */
  	mrpt::math::TPose3D getParticlePose(int i) const;
  
  	/** Save PDF's m_particles to a text file. In each line it will go: "x y z"
  	 */
  	bool saveToTextFile(const std::string& file) const override;
  
  	/** Get the m_particles count (equivalent to "particlesCount") */
  	size_t size() const { return m_particles.size(); }
  	/** this = p (+) this. This can be used to convert a PDF from local
  	 * coordinates to global, providing the point (newReferenceBase) from which
  	 *   "to project" the current pdf. Result PDF substituted the currently
  	 * stored one in the object. */
  	void changeCoordinatesReference(const CPose3D& newReferenceBase) override;
  	/** Draws a single sample from the distribution (WARNING: weights are
  	 * assumed to be normalized!) */
  	void drawSingleSample(CPose3D& outPart) const override;
  	/** Draws a number of samples from the distribution, and saves as a list of
  	 * 1x6 vectors, where each row contains a (x,y,phi) datum. */
  	void drawManySamples(
  		size_t N,
  		std::vector<mrpt::math::CVectorDouble>& outSamples) const override;
  	/** Appends (pose-composition) a given pose "p" to each particle */
  	void operator+=(const CPose3D& Ap);
  	/** Appends (add to the list) a set of m_particles to the existing ones, and
  	 * then normalize weights. */
  	void append(CPose3DPDFParticles& o);
  	/** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF */
  	void inverse(CPose3DPDF& o) const override;
  	/** Returns the particle with the highest weight. */
  	mrpt::math::TPose3D getMostLikelyParticle() const;
  	/** Bayesian fusion */
  	void bayesianFusion(const CPose3DPDF& p1, const CPose3DPDF& p2) override;
  
  };  // End of class def.
  }  // namespace mrpt::poses
  