CProbabilityDensityFunction.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2017, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
#ifndef CProbabilityDensityFunction_H
#define CProbabilityDensityFunction_H
 
#include <mrpt/math/CMatrixTemplateNumeric.h>
#include <mrpt/math/CMatrixFixedNumeric.h>
#include <mrpt/math/math_frwds.h>
 
namespace mrpt
{
namespace utils
{
/** A generic template for probability density distributions (PDFs).
 * This template is used as base for many classes in mrpt::poses
 *  Any derived class must implement \a getMean() and a getCovarianceAndMean().
 *  Other methods such as \a getMean() or \a getCovariance() are implemented
 * here for convenience.
 * \sa mprt::poses::CPosePDF, mprt::poses::CPose3DPDF, mprt::poses::CPointPDF
 * \ingroup mrpt_base_grp
 */
template <class TDATA, size_t STATE_LEN>
class CProbabilityDensityFunction
{
   public:
        /** The length of the variable, for example, 3 for a 3D point, 6 for a 3D
         * pose (x y z yaw pitch roll). */
        static const size_t state_length = STATE_LEN;
        /** The type of the state the PDF represents */
        typedef TDATA type_value;
        typedef CProbabilityDensityFunction<TDATA, STATE_LEN> self_t;
 
        /** Returns the mean, or mathematical expectation of the probability density
         * distribution (PDF).
          * \sa getCovarianceAndMean, getInformationMatrix
          */
        virtual void getMean(TDATA& mean_point) const = 0;
 
        /** Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN
         * cov matrix) and the mean, both at once.
          * \sa getMean, getInformationMatrix
          */
        virtual void getCovarianceAndMean(
                mrpt::math::CMatrixFixedNumeric<double, STATE_LEN, STATE_LEN>& cov,
                TDATA& mean_point) const = 0;
 
        /** Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN
         * cov matrix) and the mean, both at once.
          * \sa getMean, getInformationMatrix
          */
        inline void getCovarianceDynAndMean(
                mrpt::math::CMatrixDouble& cov, TDATA& mean_point) const
        {
                mrpt::math::CMatrixFixedNumeric<double, STATE_LEN, STATE_LEN> C(
                        mrpt::math::UNINITIALIZED_MATRIX);
                this->getCovarianceAndMean(C, mean_point);
                cov = C;  // Convert to dynamic size matrix
        }
 
        /** Returns the mean, or mathematical expectation of the probability density
         * distribution (PDF).
           * \sa getCovariance, getInformationMatrix
           */
        inline TDATA getMeanVal() const
        {
                TDATA p;
                getMean(p);
                return p;
        }
 
        /** Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN
         * covariance matrix)
          * \sa getMean, getCovarianceAndMean, getInformationMatrix
          */
        inline void getCovariance(mrpt::math::CMatrixDouble& cov) const
        {
                TDATA p;
                this->getCovarianceDynAndMean(cov, p);
        }
 
        /** Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN
         * covariance matrix)
          * \sa getMean, getCovarianceAndMean, getInformationMatrix
          */
        inline void getCovariance(
                mrpt::math::CMatrixFixedNumeric<double, STATE_LEN, STATE_LEN>& cov)
                const
        {
                TDATA p;
                this->getCovarianceAndMean(cov, p);
        }
 
        /** Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN
         * covariance matrix)
          * \sa getMean, getInformationMatrix
          */
        inline mrpt::math::CMatrixFixedNumeric<double, STATE_LEN, STATE_LEN>
                getCovariance() const
        {
                mrpt::math::CMatrixFixedNumeric<double, STATE_LEN, STATE_LEN> cov(
                        mrpt::math::UNINITIALIZED_MATRIX);
                TDATA p;
                this->getCovarianceAndMean(cov, p);
                return cov;
        }
 
        /** Returns whether the class instance holds the uncertainty in covariance
         * or information form.
         * \note By default this is going to be covariance form. *Inf classes
         * (e.g. CPosePDFGaussianInf) store it in information form.
         *
         * \sa mrpt::traits::is_inf_type
         */
        virtual bool isInfType() const { return false; }
        /** Returns the information (inverse covariance) matrix (a STATE_LEN x
         * STATE_LEN matrix)
          *  Unless reimplemented in derived classes, this method first reads the
         * covariance, then invert it.
          * \sa getMean, getCovarianceAndMean
          */
        virtual void getInformationMatrix(
                mrpt::math::CMatrixFixedNumeric<double, STATE_LEN, STATE_LEN>& inf)
                const
        {
                mrpt::math::CMatrixFixedNumeric<double, STATE_LEN, STATE_LEN> cov(
                        mrpt::math::UNINITIALIZED_MATRIX);
                TDATA p;
                this->getCovarianceAndMean(cov, p);
                cov.inv_fast(
                        inf);  // Destroy source cov matrix, since we don't need it anymore.
        }
 
        /** Save PDF's particles to a text file. See derived classes for more
         * information about the format of generated files.
        */
        virtual void saveToTextFile(const std::string& file) const = 0;
 
        /** Draws a single sample from the distribution
          */
        virtual void drawSingleSample(TDATA& outPart) const = 0;
 
        /** Draws a number of samples from the distribution, and saves as a list of
         * 1xSTATE_LEN vectors, where each row contains a (x,y,z,yaw,pitch,roll)
         * datum.
          * This base method just call N times to drawSingleSample, but derived
         * classes should implemented optimized method for each particular PDF.
          */
        virtual void drawManySamples(
                size_t N, std::vector<mrpt::math::CVectorDouble>& outSamples) const
        {
                outSamples.resize(N);
                TDATA pnt;
                for (size_t i = 0; i < N; i++)
                {
                        this->drawSingleSample(pnt);
                        pnt.getAsVector(outSamples[i]);
                }
        }
 
        /** 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.
          */
        virtual void changeCoordinatesReference(
                const mrpt::poses::CPose3D& newReferenceBase) = 0;
 
        /** Compute the entropy of the estimated covariance matrix.
          * \sa
         * http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Entropy
          */
        inline double getCovarianceEntropy() const
        {
                static const double ln_2PI = 1.8378770664093454835606594728112;
                return 0.5 * (STATE_LEN + STATE_LEN * ln_2PI +
                                          log(std::max(
                                                  getCovariance().det(),
                                                  std::numeric_limits<double>::epsilon())));
        }
 
};  // End of class def.
 
}  // End of namespace
}  // End of namespace
 
#endif