CProbabilityDensityFunction.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, 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 |
   +------------------------------------------------------------------------+ */
#pragma once
 
#include <mrpt/math/CMatrixTemplateNumeric.h>
#include <mrpt/math/CMatrixFixedNumeric.h>
#include <mrpt/math/math_frwds.h>
 
namespace mrpt::math
{
/** 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_math_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 constexpr size_t state_length = STATE_LEN;
    /** The type of the state the PDF represents */
    using type_value = TDATA;
    using self_t = CProbabilityDensityFunction<TDATA, STATE_LEN>;
 
    /** 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.
     * \return false on error
     */
    virtual bool 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]);
        }
    }
 
    /** Compute the entropy of the estimated covariance matrix.
     * \sa
     * http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Entropy
     */
    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.
 
}  // namespace mrpt::math