/* +------------------------------------------------------------------------+
   |                     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/math/CMatrixFixed.h>
#include <mrpt/poses/CPosePDF.h>

namespace mrpt::poses
{
class CPose3DPDF;

// This must be added to any CSerializable derived class:

/** A Probability Density  function (PDF) of a 2D pose \f$ p(\mathbf{x}) = [x ~
 * y ~ \phi ]^t \f$ as a Gaussian with a mean and the inverse of the covariance.
 *
 *   This class implements a PDF as a mono-modal Gaussian distribution in its
 * <b>information form</b>, that is,
 *     keeping the inverse of the covariance matrix instead of the covariance
 * matrix itself.
 *
 *  This class is the dual of CPosePDFGaussian.
 *
 * \sa CPose2D, CPosePDF, CPosePDFParticles
 * \ingroup poses_pdf_grp
 */
class CPosePDFGaussianInf : public CPosePDF
{
  // This must be added to any CSerializable derived class:
  DEFINE_SERIALIZABLE(CPosePDFGaussianInf, mrpt::poses)
  DEFINE_SCHEMA_SERIALIZABLE()
  using self_t = CPosePDFGaussianInf;

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

 public:
  /** @name Data fields
    @{ */

  /** The mean value */
  CPose2D mean;
  /** The inverse of the 3x3 covariance matrix (the "information" matrix) */
  mrpt::math::CMatrixDouble33 cov_inv;

  /** @} */

  inline const CPose2D& getPoseMean() const { return mean; }
  inline CPose2D& getPoseMean() { return mean; }
  /** Default constructor (mean=all zeros, inverse covariance=all zeros -> so
   * be careful!) */
  CPosePDFGaussianInf();

  /** Constructor with a mean value (inverse covariance=all zeros -> so be
   * careful!) */
  explicit CPosePDFGaussianInf(const CPose2D& init_Mean);

  /** Constructor */
  CPosePDFGaussianInf(const CPose2D& init_Mean, const mrpt::math::CMatrixDouble33& init_CovInv);

  /** Copy constructor, including transformations between other PDFs */
  explicit CPosePDFGaussianInf(const CPosePDF& o) { copyFrom(o); }
  /** Copy constructor, including transformations between other PDFs */
  explicit CPosePDFGaussianInf(const CPose3DPDF& o) { copyFrom(o); }
  /** Returns an estimate of the pose, (the mean, or mathematical expectation
   * of the PDF).
   * \sa getCovariance */
  void getMean(CPose2D& mean_pose) const override { mean_pose = mean; }
  bool isInfType() const override { return true; }

  std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const override
  {
    return {cov_inv.inverse_LLt(), mean};
  }

  /** Returns the information (inverse covariance) matrix (a STATE_LEN x
   * STATE_LEN matrix) \sa getMean, getCovarianceAndMean */
  void getInformationMatrix(mrpt::math::CMatrixDouble33& inf) const override { inf = cov_inv; }

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

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

  /** Save PDF's particles to a text file, containing the 2D pose in the first
   * line, then the covariance matrix in next 3 lines. */
  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;

  /** 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 CPose2D& newReferenceBase);

  /** Rotate the covariance matrix by replacing it by \f$
   * \mathbf{R}~\mathbf{COV}~\mathbf{R}^t \f$, where \f$ \mathbf{R} = \left[
   * \begin{array}{ccc} \cos\alpha & -\sin\alpha & 0 \\ \sin\alpha &
   * \cos\alpha & 0 \\ 0 & 0 & 1 \end{array}\right] \f$. */
  void rotateCov(const double ang);

  /** Set \f$ this = x1 \ominus x0 \f$ , computing the mean using the "-"
   * operator and the covariances through the corresponding Jacobians (For
   * 'x0' and 'x1' being independent variables!).  */
  void inverseComposition(const CPosePDFGaussianInf& x, const CPosePDFGaussianInf& ref);

  /** Set \f$ this = x1 \ominus x0 \f$ , computing the mean using the "-"
   * operator and the covariances through the corresponding Jacobians (Given
   * the 3x3 cross-covariance matrix of variables x0 and x1). */
  void inverseComposition(
      const CPosePDFGaussianInf& x1,
      const CPosePDFGaussianInf& x0,
      const mrpt::math::CMatrixDouble33& COV_01);

  /** Draws a single sample from the distribution */
  void drawSingleSample(CPose2D& outPart) const override;

  /** Draws a number of samples from the distribution, and saves as a list of
   * 1x3 vectors, where each row contains a (x,y,phi) datum. */
  void drawManySamples(size_t N, std::vector<mrpt::math::CVectorDouble>& outSamples) const override;

  /** Bayesian fusion of two points gauss. distributions, then save the result
   *in this object.
   *  The process is as follows:<br>
   *		- (x1,S1): Mean and variance of the p1 distribution.
   *		- (x2,S2): Mean and variance of the p2 distribution.
   *		- (x,S): Mean and variance of the resulting distribution.
   *
   *    \f$ S = (S_1^{-1} + S_2^{-1})^{-1} \f$
   *    \f$ x = S ( S_1^{-1} x_1 + S_2^{-1} x_2 ) \f$
   */
  void bayesianFusion(
      const CPosePDF& p1, const CPosePDF& p2, const double minMahalanobisDistToDrop = 0) override;

  /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF */
  void inverse(CPosePDF& o) const override;

  /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the
   * mean, and the covariance matrix are updated). */
  void operator+=(const CPose2D& Ap);

  /** Evaluates the PDF at a given point */
  double evaluatePDF(const CPose2D& x) const;

  /** Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in
   * the range [0,1]. */
  double evaluateNormalizedPDF(const CPose2D& x) const;

  /** Computes the Mahalanobis distance between the centers of two Gaussians.
   */
  double mahalanobisDistanceTo(const CPosePDFGaussianInf& theOther);

  /** Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the
   * mean, and the covariance matrix are updated) (see formulas in
   * jacobiansPoseComposition ). */
  void operator+=(const CPosePDFGaussianInf& Ap);

  /** Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition
   * (both the mean, and the covariance matrix are updated) */
  inline void operator-=(const CPosePDFGaussianInf& ref) { this->inverseComposition(*this, ref); }

};  // End of class def.

bool operator==(const CPosePDFGaussianInf& p1, const CPosePDFGaussianInf& p2);
/** Pose compose operator: RES = A (+) B , computing both the mean and the
 * covariance */
CPosePDFGaussianInf operator+(const CPosePDFGaussianInf& a, const CPosePDFGaussianInf& b);
/** Pose inverse compose operator: RES = A (-) B , computing both the mean and
 * the covariance */
CPosePDFGaussianInf operator-(const CPosePDFGaussianInf& a, const CPosePDFGaussianInf& b);
/** Returns the Gaussian distribution of \f$ \mathbf{C} \f$, for \f$ \mathbf{C}
 * = \mathbf{A} \oplus \mathbf{B} \f$. */
poses::CPosePDFGaussianInf operator+(
    const mrpt::poses::CPose2D& A, const mrpt::poses::CPosePDFGaussianInf& B);

/** Dumps the mean and covariance matrix to a text stream. */
std::ostream& operator<<(std::ostream& out, const CPosePDFGaussianInf& obj);

}  // namespace mrpt::poses
