template class mrpt::math::CProbabilityDensityFunction

Overview

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 getMean() and a getCovarianceAndMean(). Other methods such as getMean() or getCovariance() are implemented here for convenience.

See also:

mprt::poses::CPosePDF, mprt::poses::CPose3DPDF, mprt::poses::CPointPDF

#include <mrpt/math/CProbabilityDensityFunction.h>

template <class TDATA, size_t STATE_LEN>
class CProbabilityDensityFunction
{
public:
    // typedefs

    typedef TDATA type_value;
    typedef CProbabilityDensityFunction<TDATA, STATE_LEN> self_t;
    typedef mrpt::math::CMatrixFixed<double, STATE_LEN, STATE_LEN> cov_mat_t;
    typedef cov_mat_t inf_mat_t;

    // fields

    static constexpr size_t state_length = STATE_LEN;

    // construction

    CProbabilityDensityFunction();
    CProbabilityDensityFunction(const CProbabilityDensityFunction&);
    CProbabilityDensityFunction(CProbabilityDensityFunction&&);

    // methods

    CProbabilityDensityFunction& operator = (const CProbabilityDensityFunction&);
    CProbabilityDensityFunction& operator = (CProbabilityDensityFunction&&);
    virtual void getMean(type_value& mean_point) const = 0;
    virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const = 0;
    virtual void getCovarianceAndMean(cov_mat_t& c, TDATA& mean) const;
    void getCovarianceDynAndMean(mrpt::math::CMatrixDouble& cov, type_value& mean_point) const;
    type_value getMeanVal() const;
    void getCovariance(mrpt::math::CMatrixDouble& cov) const;
    void getCovariance(cov_mat_t& cov) const;
    cov_mat_t getCovariance() const;
    virtual bool isInfType() const;
    virtual void getInformationMatrix(inf_mat_t& inf) const;
    virtual bool saveToTextFile(const std::string& file) const = 0;
    virtual void drawSingleSample(TDATA& outPart) const = 0;
    virtual void drawManySamples(size_t N, std::vector<mrpt::math::CVectorDouble>& outSamples) const;
    double getCovarianceEntropy() const;
};

// direct descendants

class CPoint2DPDF;
class CPointPDF;
class CPose3DPDF;
class CPose3DQuatPDF;
class CPosePDF;

Typedefs

typedef TDATA type_value

The type of the state the PDF represents.

typedef mrpt::math::CMatrixFixed<double, STATE_LEN, STATE_LEN> cov_mat_t

Covariance matrix type.

typedef cov_mat_t inf_mat_t

Information matrix type.

Fields

static constexpr size_t state_length = STATE_LEN

The length of the variable, for example, 3 for a 3D point, 6 for a 3D pose (x y z yaw pitch roll).

Methods

virtual void getMean(type_value& mean_point) const = 0

Returns the mean, or mathematical expectation of the probability density distribution (PDF).

virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const = 0

Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.

See also:

getMean, getInformationMatrix

virtual void getCovarianceAndMean(cov_mat_t& c, TDATA& mean) const

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

void getCovarianceDynAndMean(mrpt::math::CMatrixDouble& cov, type_value& mean_point) const

Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.

See also:

getMean, getInformationMatrix

type_value getMeanVal() const

Returns the mean, or mathematical expectation of the probability density distribution (PDF).

See also:

getCovariance, getInformationMatrix

void getCovariance(mrpt::math::CMatrixDouble& cov) const

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

void getCovariance(cov_mat_t& cov) const

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

cov_mat_t getCovariance() const

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

See also:

getMean, getInformationMatrix

virtual bool isInfType() const

Returns whether the class instance holds the uncertainty in covariance or information form.

By default this is going to be covariance form. *Inf classes (e.g. CPosePDFGaussianInf) store it in information form.

See also:

mrpt::traits::is_inf_type

virtual void getInformationMatrix(inf_mat_t& inf) const

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.

See also:

getMean, getCovarianceAndMean

virtual bool saveToTextFile(const std::string& file) const = 0

Save PDF’s particles to a text file.

See derived classes for more information about the format of generated files.

Returns:

false on error

virtual void drawSingleSample(TDATA& outPart) const = 0

Draws a single sample from the distribution.

virtual void drawManySamples(size_t N, std::vector<mrpt::math::CVectorDouble>& outSamples) const

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.

double getCovarianceEntropy() const

Compute the entropy of the estimated covariance matrix.