/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2024, 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/core/aligned_allocator.h>
#include <mrpt/poses/CPose3DPDF.h>
#include <mrpt/poses/CPose3DPDFGaussian.h>

namespace mrpt::poses
{
/** Declares a class that represents a Probability Density function (PDF) of a
 * 3D(6D) pose \f$ p(\mathbf{x}) = [x ~ y ~ z ~ yaw ~ pitch ~ roll]^t \f$.
 *   This class implements that PDF as the following multi-modal Gaussian
 * distribution:
 *
 * \f$ p(\mathbf{x}) = \sum\limits_{i=1}^N \omega^i \mathcal{N}( \mathbf{x} ;
 * \bar{\mathbf{x}}^i, \mathbf{\Sigma}^i )  \f$
 *
 *  Where the number of modes N is the size of CPose3DPDFSOG::m_modes. Angles
 * are always in radians.
 *
 *  See mrpt::poses::CPose3DPDF for more details.
 * \ingroup poses_pdf_grp
 * \sa CPose3DPDF
 */
class CPose3DPDFSOG : public CPose3DPDF
{
  DEFINE_SERIALIZABLE(CPose3DPDFSOG, mrpt::poses)

 public:
  /** The struct for each mode:
   */
  struct TGaussianMode
  {
    TGaussianMode() : val() {}
    CPose3DPDFGaussian val;

    /** The log-weight
     */
    double log_w{0};
  };

  using TModesList = std::vector<TGaussianMode, mrpt::aligned_allocator_cpp11<TGaussianMode>>;
  using const_iterator = TModesList::const_iterator;
  using iterator = TModesList::iterator;

 protected:
  /** Assures the symmetry of the covariance matrix (eventually certain
   * operations in the math-coprocessor lead to non-symmetric matrixes!)
   */
  void enforceCovSymmetry();

  /** Access directly to this array for modify the modes as desired.
   *  Note that no weight can be zero!!
   *  We must use pointers to satisfy the mem-alignment of the matrixes
   */
  TModesList m_modes;

 public:
  /** Default constructor
   * \param nModes The initial size of CPose3DPDFSOG::m_modes
   */
  CPose3DPDFSOG(size_t nModes = 1);

  /** Clear all the gaussian modes */
  void clear();
  /** Set the number of SOG modes */
  void resize(size_t N);
  /** Return the number of Gaussian modes. */
  size_t size() const { return m_modes.size(); }
  /** Return whether there is any Gaussian mode. */
  bool empty() const { return m_modes.empty(); }
  iterator begin() { return m_modes.begin(); }
  iterator end() { return m_modes.end(); }
  const_iterator begin() const { return m_modes.begin(); }
  const_iterator end() const { return m_modes.end(); }

  void getMean(CPose3D& mean_pose) const override;
  std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const override;

  /** Normalize the weights in m_modes such as the maximum log-weight is 0. */
  void normalizeWeights();
  /** Return the Gaussian mode with the highest likelihood (or an empty
   * Gaussian if there are no modes in this SOG) */
  void getMostLikelyMode(CPose3DPDFGaussian& outVal) const;

  /** Copy operator, translating if necessary (for example, between particles
   * and gaussian representations) */
  void copyFrom(const CPose3DPDF& o) override;

  /** Save the density to a text file, with the following format:
   *  There is one row per Gaussian "mode", and each row contains 10
   * elements:
   *   - w (The linear weight)
   *   - x_mean (gaussian mean value)
   *   - y_mean (gaussian mean value)
   *   - x_mean (gaussian mean value)
   *   - yaw_mean (gaussian mean value, in radians)
   *   - pitch_mean (gaussian mean value, in radians)
   *   - roll_mean (gaussian mean value, in radians)
   *   - C11,C22,C33,C44,C55,C66 (Covariance elements)
   *   - C12,C13,C14,C15,C16 (Covariance elements)
   *   - C23,C24,C25,C25 (Covariance elements)
   *   - C34,C35,C36 (Covariance elements)
   *   - C45,C46 (Covariance elements)
   *   - C56 (Covariance elements)
   *
   */
  bool saveToTextFile(const std::string& file) const override;

  /** 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;

  /** Bayesian fusion of two pose distributions, then save the result in this
   * object (WARNING: Currently p1 must be a mrpt::poses::CPose3DPDFSOG object
   * and p2 a mrpt::poses::CPose3DPDFSOG object) */
  void bayesianFusion(const CPose3DPDF& p1, const CPose3DPDF& p2) override;

  /** Draws a single sample from the distribution */
  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,z,yaw,pitch,roll) datum */
  void drawManySamples(size_t N, std::vector<mrpt::math::CVectorDouble>& outSamples) const override;
  /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF */
  void inverse(CPose3DPDF& o) const override;

  /** Append the Gaussian modes from "o" to the current set of modes of "this"
   * density */
  void appendFrom(const CPose3DPDFSOG& o);

};  // End of class def.
}  // namespace mrpt::poses
