Template Class CProbabilityDensityFunction

Defined in File CProbabilityDensityFunction.h

Class Documentation

template<class TDATA, size_t STATE_LEN> class CProbabilityDensityFunction

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

Public Types

using type_value = TDATA : The type of the state the PDF represents

using self_t = CProbabilityDensityFunction<TDATA, STATE_LEN>

using cov_mat_t = mrpt::math::CMatrixFixed<double, STATE_LEN, STATE_LEN>: Covariance matrix type

using inf_mat_t = cov_mat_t : Information matrix type

Public Functions

virtual ~CProbabilityDensityFunction() = default

CProbabilityDensityFunction() = default

CProbabilityDensityFunction(const CProbabilityDensityFunction&) = default

CProbabilityDensityFunction(CProbabilityDensityFunction&&) = default

CProbabilityDensityFunction &operator=(const CProbabilityDensityFunction&) = default

CProbabilityDensityFunction &operator=(CProbabilityDensityFunction&&) = default

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

inline virtual void getCovarianceAndMean(cov_mat_t &c, TDATA &mean) const final: This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

inline 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

inline type_value getMeanVal() const

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

See also

getCovariance, getInformationMatrix

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

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

inline void getCovariance(cov_mat_t &cov) const

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

inline cov_mat_t getCovariance() const

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

See also

getMean, getInformationMatrix

inline virtual bool isInfType() const

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

See also

mrpt::traits::is_inf_type

Note

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

inline 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

inline 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.

inline double getCovarianceEntropy() const

Compute the entropy of the estimated covariance matrix.

Public Static Attributes

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).