Detailed Description

Collaboration diagram for Statistics functions, probability distributions:

Functions
double	mrpt::math::normalPDF (double x, double mu, double std)
	Evaluates the univariate normal (Gaussian) distribution at a given point "x". More...

template<class VECTORLIKE1 , class VECTORLIKE2 , class MATRIXLIKE >
MATRIXLIKE::Scalar	mrpt::math::normalPDFInf (const VECTORLIKE1 &x, const VECTORLIKE2 &mu, const MATRIXLIKE &cov_inv, const bool scaled_pdf=false)
	Evaluates the multivariate normal (Gaussian) distribution at a given point "x". More...

template<class VECTORLIKE1 , class VECTORLIKE2 , class MATRIXLIKE >
MATRIXLIKE::Scalar	mrpt::math::normalPDF (const VECTORLIKE1 &x, const VECTORLIKE2 &mu, const MATRIXLIKE &cov, const bool scaled_pdf=false)
	Evaluates the multivariate normal (Gaussian) distribution at a given point "x". More...

template<typename VECTORLIKE , typename MATRIXLIKE >
MATRIXLIKE::Scalar	mrpt::math::normalPDF (const VECTORLIKE &d, const MATRIXLIKE &cov)
	Evaluates the multivariate normal (Gaussian) distribution at a given point given its distance vector "d" from the Gaussian mean. More...

template<typename VECTORLIKE1 , typename MATRIXLIKE1 , typename VECTORLIKE2 , typename MATRIXLIKE2 >
double	mrpt::math::KLD_Gaussians (const VECTORLIKE1 &mu0, const MATRIXLIKE1 &cov0, const VECTORLIKE2 &mu1, const MATRIXLIKE2 &cov1)
	Kullback-Leibler divergence (KLD) between two independent multivariate Gaussians. More...

double	mrpt::math::normalQuantile (double p)
	Evaluates the Gaussian distribution quantile for the probability value p=[0,1]. More...

double	mrpt::math::normalCDF (double p)
	Evaluates the Gaussian cumulative density function. More...

double	mrpt::math::chi2inv (double P, unsigned int dim=1)
	The "quantile" of the Chi-Square distribution, for dimension "dim" and probability 0<P<1 (the inverse of chi2CDF) An aproximation from the Wilson-Hilferty transformation is used. More...

double	mrpt::math::noncentralChi2CDF (unsigned int degreesOfFreedom, double noncentrality, double arg)

double	mrpt::math::chi2CDF (unsigned int degreesOfFreedom, double arg)

double	mrpt::math::chi2PDF (unsigned int degreesOfFreedom, double arg, double accuracy=1e-7)

std::pair< double, double >	mrpt::math::noncentralChi2PDF_CDF (unsigned int degreesOfFreedom, double noncentrality, double arg, double eps=1e-7)
	Returns the 'exact' PDF (first) and CDF (second) of a Non-central chi-squared probability distribution, using an iterative method. More...

template<typename CONTAINER >
void	mrpt::math::confidenceIntervals (const CONTAINER &data, typename mrpt::math::ContainerType< CONTAINER >::element_t &out_mean, typename mrpt::math::ContainerType< CONTAINER >::element_t &out_lower_conf_interval, typename mrpt::math::ContainerType< CONTAINER >::element_t &out_upper_conf_interval, const double confidenceInterval=0.1, const size_t histogramNumBins=1000)
	Return the mean and the 10%-90% confidence points (or with any other confidence value) of a set of samples by building the cummulative CDF of all the elements of the container. More...

std::string	mrpt::math::MATLAB_plotCovariance2D (const CMatrixFloat &cov22, const CVectorFloat &mean, const float &stdCount, const std::string &style=std::string("b"), const size_t &nEllipsePoints=30)
	Generates a string with the MATLAB commands required to plot an confidence interval (ellipse) for a 2D Gaussian ('float' version). More...

std::string	mrpt::math::MATLAB_plotCovariance2D (const CMatrixDouble &cov22, const CVectorDouble &mean, const float &stdCount, const std::string &style=std::string("b"), const size_t &nEllipsePoints=30)
	Generates a string with the MATLAB commands required to plot an confidence interval (ellipse) for a 2D Gaussian ('double' version). More...

Probability density distributions (pdf) distance metrics
template<class VECTORLIKE1 , class VECTORLIKE2 , class MAT >
MAT::Scalar	mrpt::math::mahalanobisDistance2 (const VECTORLIKE1 &X, const VECTORLIKE2 &MU, const MAT &COV)
	Computes the squared mahalanobis distance of a vector X given the mean MU and the covariance inverse COV_inv $d^2 = (X-MU)^\top \Sigma^{-1} (X-MU)$ . More...

template<class VECTORLIKE1 , class VECTORLIKE2 , class MAT >
VECTORLIKE1::Scalar	mrpt::math::mahalanobisDistance (const VECTORLIKE1 &X, const VECTORLIKE2 &MU, const MAT &COV)
	Computes the mahalanobis distance of a vector X given the mean MU and the covariance inverse COV_inv $d = \sqrt{ (X-MU)^\top \Sigma^{-1} (X-MU) }$ . More...

template<class VECTORLIKE , class MAT1 , class MAT2 , class MAT3 >
MAT1::Scalar	mrpt::math::mahalanobisDistance2 (const VECTORLIKE &mean_diffs, const MAT1 &COV1, const MAT2 &COV2, const MAT3 &CROSS_COV12)
	Computes the squared mahalanobis distance between two non-independent Gaussians, given the two covariance matrices and the vector with the difference of their means. More...

template<class VECTORLIKE , class MAT1 , class MAT2 , class MAT3 >
VECTORLIKE::Scalar	mrpt::math::mahalanobisDistance (const VECTORLIKE &mean_diffs, const MAT1 &COV1, const MAT2 &COV2, const MAT3 &CROSS_COV12)
	Computes the mahalanobis distance between two non-independent Gaussians (or independent if CROSS_COV12=nullptr), given the two covariance matrices and the vector with the difference of their means. More...

template<class VECTORLIKE , class MATRIXLIKE >
MATRIXLIKE::Scalar	mrpt::math::mahalanobisDistance2 (const VECTORLIKE &delta_mu, const MATRIXLIKE &cov)
	Computes the squared mahalanobis distance between a point and a Gaussian, given the covariance matrix and the vector with the difference between the mean and the point. More...

template<class VECTORLIKE , class MATRIXLIKE >
MATRIXLIKE::Scalar	mrpt::math::mahalanobisDistance (const VECTORLIKE &delta_mu, const MATRIXLIKE &cov)
	Computes the mahalanobis distance between a point and a Gaussian, given the covariance matrix and the vector with the difference between the mean and the point. More...

template<typename T >
T	mrpt::math::productIntegralTwoGaussians (const std::vector< T > &mean_diffs, const CMatrixTemplateNumeric< T > &COV1, const CMatrixTemplateNumeric< T > &COV2)
	Computes the integral of the product of two Gaussians, with means separated by "mean_diffs" and covariances "COV1" and "COV2". More...

template<typename T , size_t DIM>
T	mrpt::math::productIntegralTwoGaussians (const std::vector< T > &mean_diffs, const CMatrixFixedNumeric< T, DIM, DIM > &COV1, const CMatrixFixedNumeric< T, DIM, DIM > &COV2)
	Computes the integral of the product of two Gaussians, with means separated by "mean_diffs" and covariances "COV1" and "COV2". More...

template<typename T , class VECLIKE , class MATLIKE1 , class MATLIKE2 >
void	mrpt::math::productIntegralAndMahalanobisTwoGaussians (const VECLIKE &mean_diffs, const MATLIKE1 &COV1, const MATLIKE2 &COV2, T &maha2_out, T &intprod_out, const MATLIKE1 *CROSS_COV12=nullptr)
	Computes both, the integral of the product of two Gaussians and their square Mahalanobis distance. More...

template<typename T , class VECLIKE , class MATRIXLIKE >
void	mrpt::math::mahalanobisDistance2AndLogPDF (const VECLIKE &diff_mean, const MATRIXLIKE &cov, T &maha2_out, T &log_pdf_out)
	Computes both, the logarithm of the PDF and the square Mahalanobis distance between a point (given by its difference wrt the mean) and a Gaussian. More...

template<typename T , class VECLIKE , class MATRIXLIKE >
void	mrpt::math::mahalanobisDistance2AndPDF (const VECLIKE &diff_mean, const MATRIXLIKE &cov, T &maha2_out, T &pdf_out)
	Computes both, the PDF and the square Mahalanobis distance between a point (given by its difference wrt the mean) and a Gaussian. More...

template<class VECTOR_OF_VECTORS , class MATRIXLIKE , class VECTORLIKE , class VECTORLIKE2 , class VECTORLIKE3 >
void	mrpt::math::covariancesAndMeanWeighted (const VECTOR_OF_VECTORS &elements, MATRIXLIKE &covariances, VECTORLIKE &means, const VECTORLIKE2 weights_mean, const VECTORLIKE3 weights_cov, const bool *elem_do_wrap2pi=nullptr)
	Computes covariances and mean of any vector of containers, given optional weights for the different samples. More...

template<class VECTOR_OF_VECTORS , class MATRIXLIKE , class VECTORLIKE >
void	mrpt::math::covariancesAndMean (const VECTOR_OF_VECTORS &elements, MATRIXLIKE &covariances, VECTORLIKE &means, const bool *elem_do_wrap2pi=nullptr)
	Computes covariances and mean of any vector of containers. More...

template<class VECTORLIKE1 , class VECTORLIKE2 >
void	mrpt::math::weightedHistogram (const VECTORLIKE1 &values, const VECTORLIKE1 &weights, float binWidth, VECTORLIKE2 &out_binCenters, VECTORLIKE2 &out_binValues)
	Computes the weighted histogram for a vector of values and their corresponding weights. More...

template<class VECTORLIKE1 , class VECTORLIKE2 >
void	mrpt::math::weightedHistogramLog (const VECTORLIKE1 &values, const VECTORLIKE1 &log_weights, float binWidth, VECTORLIKE2 &out_binCenters, VECTORLIKE2 &out_binValues)
	Computes the weighted histogram for a vector of values and their corresponding log-weights. More...

double	mrpt::math::averageLogLikelihood (const CVectorDouble &logLikelihoods)
	A numerically-stable method to compute average likelihood values with strongly different ranges (unweighted likelihoods: compute the arithmetic mean). More...

double	mrpt::math::averageWrap2Pi (const CVectorDouble &angles)
	Computes the average of a sequence of angles in radians taking into account the correct wrapping in the range $]-\pi,\pi [$ , for example, the mean of (2,-2) is $\pi$ , not 0. More...

double	mrpt::math::averageLogLikelihood (const CVectorDouble &logWeights, const CVectorDouble &logLikelihoods)
	A numerically-stable method to average likelihood values with strongly different ranges (weighted likelihoods). More...

Function Documentation

◆ averageLogLikelihood() [1/2]

double mrpt::math::averageLogLikelihood ( const CVectorDouble & logLikelihoods )

A numerically-stable method to compute average likelihood values with strongly different ranges (unweighted likelihoods: compute the arithmetic mean).

This method implements this equation:

$return = - \log N + \log \sum_{i=1}^N e^{ll_i-ll_{max}} + ll_{max}$

◆ averageLogLikelihood() [2/2]

double mrpt::math::averageLogLikelihood	(	const CVectorDouble &	logWeights,
		const CVectorDouble &	logLikelihoods
	)

A numerically-stable method to average likelihood values with strongly different ranges (weighted likelihoods).

This method implements this equation:

$return = \log \left( \frac{1}{\sum_i e^{lw_i}} \sum_i e^{lw_i} e^{ll_i} \right)$

◆ averageWrap2Pi()

double mrpt::math::averageWrap2Pi ( const CVectorDouble & angles )

Computes the average of a sequence of angles in radians taking into account the correct wrapping in the range $]-\pi,\pi [$ , for example, the mean of (2,-2) is $\pi$ , not 0.

Definition at line 1799 of file math.cpp.

References M_2PI, and M_PI.

Referenced by mrpt::topography::path_from_rtk_gps().

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◆ chi2CDF()

double mrpt::mrpt::math::chi2CDF	(	unsigned int	degreesOfFreedom,
		double	arg
	)

Cumulative chi square distribution.

Computes the cumulative density of a chi square distribution with \a

degreesOfFreedom and tolerance accuracy at the given argument arg, i.e. the probability that a random number drawn from the distribution is below arg by calling noncentralChi2CDF(degreesOfFreedom, 0.0, arg, accuracy).

Note: Function code from the Vigra project (http://hci.iwr.uni-heidelberg.de/vigra/); code under "MIT X11 License", GNU GPL-compatible.

Definition at line 2043 of file math.cpp.

References mrpt::math::noncentralChi2CDF().

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◆ chi2inv()

double mrpt::math::chi2inv	(	double	P,
		unsigned int	dim = `1`
	)

The "quantile" of the Chi-Square distribution, for dimension "dim" and probability 0<P<1 (the inverse of chi2CDF) An aproximation from the Wilson-Hilferty transformation is used.

Note: Equivalent to MATLAB chi2inv(), but note that this is just an approximation, which becomes very poor for small values of "P".

Definition at line 998 of file math.cpp.

References ASSERT_, and mrpt::math::normalQuantile().

Referenced by mrpt::slam::CGridMapAligner::AlignPDF_robustMatch(), mrpt::slam::data_association_full_covariance(), mrpt::hmtslam::CHMTSLAM::LSLAM_process_message_from_TBI(), mrpt::slam::StandardProposal::PF_SLAM_implementation(), mrpt::slam::AuxiliaryPFStandardAndOptimal< USE_OPTIMAL_SAMPLING >::PF_SLAM_implementation(), mrpt::tfest::se2_l2_robust(), and TEST().

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◆ chi2PDF()

double mrpt::mrpt::math::chi2PDF	(	unsigned int	degreesOfFreedom,
		double	arg,
		double	accuracy = `1e-7`
	)

Chi square distribution PDF. Computes the density of a chi square distribution with degreesOfFreedom and tolerance accuracy at the given argument arg by calling noncentralChi2(degreesOfFreedom, 0.0, arg, accuracy).

Note: Function code from the Vigra project (http://hci.iwr.uni-heidelberg.de/vigra/); code under "MIT X11 License", GNU GPL-compatible.; Equivalent to MATLAB's chi2pdf(arg,degreesOfFreedom)

Definition at line 2133 of file math.cpp.

References mrpt::math::noncentralChi2PDF_CDF().

Referenced by TEST().

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◆ confidenceIntervals()

template<typename CONTAINER >

void mrpt::math::confidenceIntervals	(	const CONTAINER &	data,
		typename mrpt::math::ContainerType< CONTAINER >::element_t &	out_mean,
		typename mrpt::math::ContainerType< CONTAINER >::element_t &	out_lower_conf_interval,
		typename mrpt::math::ContainerType< CONTAINER >::element_t &	out_upper_conf_interval,
		const double	confidenceInterval = `0.1`,
		const size_t	histogramNumBins = `1000`
	)

Return the mean and the 10%-90% confidence points (or with any other confidence value) of a set of samples by building the cummulative CDF of all the elements of the container.

The container can be any MRPT container (CArray, matrices, vectors).

Parameters

confidenceInterval A number in the range (0,1) such as the confidence interval will be [100*confidenceInterval, 100*(1-confidenceInterval)].

Definition at line 223 of file distributions.h.

References ASSERT_, mrpt::math::cumsum(), mrpt::math::distance(), mrpt::math::histogram(), mrpt::math::maximum(), mrpt::math::mean(), mrpt::math::minimum_maximum(), MRPT_END, and MRPT_START.

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◆ covariancesAndMean()

template<class VECTOR_OF_VECTORS , class MATRIXLIKE , class VECTORLIKE >

void mrpt::math::covariancesAndMean	(	const VECTOR_OF_VECTORS &	elements,
		MATRIXLIKE &	covariances,
		VECTORLIKE &	means,
		const bool *	elem_do_wrap2pi = `nullptr`
	)

Computes covariances and mean of any vector of containers.

Parameters

elements	Any kind of vector of vectors/arrays, eg. std::vector<mrpt::math::CVectorDouble>, with all the input samples, each sample in a "row".
covariances	Output estimated covariance; it can be a fixed/dynamic matrix or a matrixview.
means	Output estimated mean; it can be CVectorDouble/CArrayDouble, etc...
elem_do_wrap2pi	If !=nullptr; it must point to an array of "bool" of size()==dimension of each element, stating if it's needed to do a wrap to [-pi,pi] to each dimension.

Definition at line 386 of file data_utils.h.

References mrpt::math::covariancesAndMeanWeighted().

Referenced by mrpt::math::getRegressionLine(), mrpt::math::getRegressionPlane(), and mrpt::math::transform_gaussian_montecarlo().

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◆ covariancesAndMeanWeighted()

template<class VECTOR_OF_VECTORS , class MATRIXLIKE , class VECTORLIKE , class VECTORLIKE2 , class VECTORLIKE3 >

void mrpt::math::covariancesAndMeanWeighted	(	const VECTOR_OF_VECTORS &	elements,
		MATRIXLIKE &	covariances,
		VECTORLIKE &	means,
		const VECTORLIKE2 *	weights_mean,
		const VECTORLIKE3 *	weights_cov,
		const bool *	elem_do_wrap2pi = `nullptr`
	)

inline

Computes covariances and mean of any vector of containers, given optional weights for the different samples.

Parameters

elements	Any kind of vector of vectors/arrays, eg. std::vector<mrpt::math::CVectorDouble>, with all the input samples, each sample in a "row".
covariances	Output estimated covariance; it can be a fixed/dynamic matrix or a matrixview.
means	Output estimated mean; it can be CVectorDouble/CArrayDouble, etc...
weights_mean	If !=nullptr, it must point to a vector of size()==number of elements, with normalized weights to take into account for the mean.
weights_cov	If !=nullptr, it must point to a vector of size()==number of elements, with normalized weights to take into account for the covariance.
elem_do_wrap2pi	If !=nullptr; it must point to an array of "bool" of size()==dimension of each element, stating if it's needed to do a wrap to [-pi,pi] to each dimension.

See also: This method is used in mrpt::math::unscented_transform_gaussian

Definition at line 263 of file data_utils.h.

References ASSERTDEB_, ASSERTMSG_, M_2PI, M_PI, and mrpt::math::wrapToPi().

Referenced by mrpt::math::covariancesAndMean(), and mrpt::math::transform_gaussian_unscented().

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◆ KLD_Gaussians()

template<typename VECTORLIKE1 , typename MATRIXLIKE1 , typename VECTORLIKE2 , typename MATRIXLIKE2 >

double mrpt::math::KLD_Gaussians	(	const VECTORLIKE1 &	mu0,
		const MATRIXLIKE1 &	cov0,
		const VECTORLIKE2 &	mu1,
		const MATRIXLIKE2 &	cov1
	)

Kullback-Leibler divergence (KLD) between two independent multivariate Gaussians.

$D_\mathrm{KL}(\mathcal{N}_0 \| \mathcal{N}_1) = { 1 \over 2 } ( \log_e ( { \det \Sigma_1 \over \det \Sigma_0 } ) + \mathrm{tr} ( \Sigma_1^{-1} \Sigma_0 ) + ( \mu_1 - \mu_0 )^\top \Sigma_1^{-1} ( \mu_1 - \mu_0 ) - N )$

Definition at line 114 of file distributions.h.

References ASSERT_, MRPT_END, MRPT_START, mrpt::math::multiply_HCHt_scalar(), and mrpt::math::size().

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◆ mahalanobisDistance() [1/3]

template<class VECTORLIKE1 , class VECTORLIKE2 , class MAT >

VECTORLIKE1::Scalar mrpt::math::mahalanobisDistance	(	const VECTORLIKE1 &	X,
		const VECTORLIKE2 &	MU,
		const MAT &	COV
	)

inline

Computes the mahalanobis distance of a vector X given the mean MU and the covariance inverse COV_inv

$d = \sqrt{ (X-MU)^\top \Sigma^{-1} (X-MU) }$

.

Definition at line 59 of file data_utils.h.

References mrpt::math::mahalanobisDistance2().

Referenced by mrpt::math::mahalanobisDistance(), mrpt::poses::CPose3DQuatPDFGaussian::mahalanobisDistanceTo(), and mrpt::topography::path_from_rtk_gps().

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◆ mahalanobisDistance() [2/3]

template<class VECTORLIKE , class MAT1 , class MAT2 , class MAT3 >

VECTORLIKE::Scalar mrpt::math::mahalanobisDistance	(	const VECTORLIKE &	mean_diffs,
		const MAT1 &	COV1,
		const MAT2 &	COV2,
		const MAT3 &	CROSS_COV12
	)

inline

Computes the mahalanobis distance between two non-independent Gaussians (or independent if CROSS_COV12=nullptr), given the two covariance matrices and the vector with the difference of their means.

$d = \sqrt{ \Delta_\mu^\top (\Sigma_1 + \Sigma_2 - 2 \Sigma_12 )^{-1} \Delta_\mu }$

Definition at line 99 of file data_utils.h.

References mrpt::math::mahalanobisDistance().

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◆ mahalanobisDistance() [3/3]

template<class VECTORLIKE , class MATRIXLIKE >

MATRIXLIKE::Scalar mrpt::math::mahalanobisDistance	(	const VECTORLIKE &	delta_mu,
		const MATRIXLIKE &	cov
	)

inline

Computes the mahalanobis distance between a point and a Gaussian, given the covariance matrix and the vector with the difference between the mean and the point.

$d^2 = \sqrt( \Delta_\mu^\top \Sigma^{-1} \Delta_\mu )$

Definition at line 126 of file data_utils.h.

References mrpt::math::cov(), and mrpt::math::mahalanobisDistance2().

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◆ mahalanobisDistance2() [1/3]

template<class VECTORLIKE1 , class VECTORLIKE2 , class MAT >

MAT::Scalar mrpt::math::mahalanobisDistance2	(	const VECTORLIKE1 &	X,
		const VECTORLIKE2 &	MU,
		const MAT &	COV
	)

Computes the squared mahalanobis distance of a vector X given the mean MU and the covariance inverse COV_inv

$d^2 = (X-MU)^\top \Sigma^{-1} (X-MU)$

.

Definition at line 36 of file data_utils.h.

References ASSERT_, MRPT_END, MRPT_START, and mrpt::math::size().

Referenced by mrpt::math::mahalanobisDistance(), and mrpt::graphslam::deciders::CLoopCloserERD< GRAPH_T >::mahalanobisDistanceOdometryToICPEdge().

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◆ mahalanobisDistance2() [2/3]

template<class VECTORLIKE , class MAT1 , class MAT2 , class MAT3 >

MAT1::Scalar mrpt::math::mahalanobisDistance2	(	const VECTORLIKE &	mean_diffs,
		const MAT1 &	COV1,
		const MAT2 &	COV2,
		const MAT3 &	CROSS_COV12
	)

Computes the squared mahalanobis distance between two non-independent Gaussians, given the two covariance matrices and the vector with the difference of their means.

$d^2 = \Delta_\mu^\top (\Sigma_1 + \Sigma_2 - 2 \Sigma_12 )^{-1} \Delta_\mu$

Definition at line 72 of file data_utils.h.

References ASSERT_, MRPT_END, MRPT_START, mrpt::math::multiply_HCHt_scalar(), and mrpt::math::size().

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◆ mahalanobisDistance2() [3/3]

template<class VECTORLIKE , class MATRIXLIKE >

MATRIXLIKE::Scalar mrpt::math::mahalanobisDistance2	(	const VECTORLIKE &	delta_mu,
		const MATRIXLIKE &	cov
	)

inline

Computes the squared mahalanobis distance between a point and a Gaussian, given the covariance matrix and the vector with the difference between the mean and the point.

$d^2 = \Delta_\mu^\top \Sigma^{-1} \Delta_\mu$

Definition at line 112 of file data_utils.h.

References ASSERTDEB_, mrpt::math::cov(), and mrpt::math::multiply_HCHt_scalar().

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◆ mahalanobisDistance2AndLogPDF()

template<typename T , class VECLIKE , class MATRIXLIKE >

void mrpt::math::mahalanobisDistance2AndLogPDF	(	const VECLIKE &	diff_mean,
		const MATRIXLIKE &	cov,
		T &	maha2_out,
		T &	log_pdf_out
	)

Computes both, the logarithm of the PDF and the square Mahalanobis distance between a point (given by its difference wrt the mean) and a Gaussian.

See also: productIntegralTwoGaussians, mahalanobisDistance2, normalPDF, mahalanobisDistance2AndPDF

Definition at line 212 of file data_utils.h.

References ASSERTDEB_, mrpt::math::cov(), M_2PI, MRPT_END, MRPT_START, and mrpt::math::multiply_HCHt_scalar().

Referenced by mrpt::slam::data_association_full_covariance(), mrpt::math::mahalanobisDistance2AndPDF(), and mrpt::slam::CRangeBearingKFSLAM2D::OnGetObservationsAndDataAssociation().

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◆ mahalanobisDistance2AndPDF()

template<typename T , class VECLIKE , class MATRIXLIKE >

void mrpt::math::mahalanobisDistance2AndPDF	(	const VECLIKE &	diff_mean,
		const MATRIXLIKE &	cov,
		T &	maha2_out,
		T &	pdf_out
	)

inline

Computes both, the PDF and the square Mahalanobis distance between a point (given by its difference wrt the mean) and a Gaussian.

See also: productIntegralTwoGaussians, mahalanobisDistance2, normalPDF

Definition at line 235 of file data_utils.h.

References mrpt::math::cov(), and mrpt::math::mahalanobisDistance2AndLogPDF().

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◆ MATLAB_plotCovariance2D() [1/2]

string mrpt::math::MATLAB_plotCovariance2D	(	const CMatrixFloat &	cov22,
		const CVectorFloat &	mean,
		const float &	stdCount,
		const std::string &	style = `std::string("b")`,
		const size_t &	nEllipsePoints = `30`
	)

Generates a string with the MATLAB commands required to plot an confidence interval (ellipse) for a 2D Gaussian ('float' version).

Parameters

cov22	The 2x2 covariance matrix
mean	The 2-length vector with the mean
stdCount	How many "quantiles" to get into the area of the ellipse: 2: 95%, 3:99.97%,...
style	A matlab style string, for colors, line styles,...
nEllipsePoints	The number of points in the ellipse to generate

Definition at line 1840 of file math.cpp.

References mrpt::math::cov(), mean(), MRPT_END, and MRPT_START.

Referenced by mrpt::slam::CRangeBearingKFSLAM2D::saveMapAndPath2DRepresentationAsMATLABFile(), and mrpt::slam::CRangeBearingKFSLAM::saveMapAndPath2DRepresentationAsMATLABFile().

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◆ MATLAB_plotCovariance2D() [2/2]

string mrpt::math::MATLAB_plotCovariance2D	(	const CMatrixDouble &	cov22,
		const CVectorDouble &	mean,
		const float &	stdCount,
		const std::string &	style = `std::string("b")`,
		const size_t &	nEllipsePoints = `30`
	)

Generates a string with the MATLAB commands required to plot an confidence interval (ellipse) for a 2D Gaussian ('double' version).

Parameters

cov22	The 2x2 covariance matrix
mean	The 2-length vector with the mean
stdCount	How many "quantiles" to get into the area of the ellipse: 2: 95%, 3:99.97%,...
style	A matlab style string, for colors, line styles,...
nEllipsePoints	The number of points in the ellipse to generate

Definition at line 1856 of file math.cpp.

References ASSERT_, mrpt::math::cov(), mrpt::format(), M_2PI, mean(), MRPT_END_WITH_CLEAN_UP, and MRPT_START.

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◆ noncentralChi2CDF()

double mrpt::mrpt::math::noncentralChi2CDF	(	unsigned int	degreesOfFreedom,
		double	noncentrality,
		double	arg
	)

Cumulative non-central chi square distribution (approximate).

Computes approximate values of the cumulative density of a chi square

distribution with degreesOfFreedom, and noncentrality parameter noncentrality at the given argument arg, i.e. the probability that a random number drawn from the distribution is below arg It uses the approximate transform into a normal distribution due to Wilson and Hilferty (see Abramovitz, Stegun: "Handbook of Mathematical Functions", formula 26.3.32). The algorithm's running time is independent of the inputs. The accuracy is only about 0.1 for few degrees of freedom, but reaches about 0.001 above dof = 5.

Note: Function code from the Vigra project (http://hci.iwr.uni-heidelberg.de/vigra/); code under "MIT X11 License", GNU GPL-compatible.

See also: noncentralChi2PDF_CDF

Definition at line 2141 of file math.cpp.

References mrpt::math::square().

Referenced by mrpt::math::chi2CDF().

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◆ noncentralChi2PDF_CDF()

std::pair< double, double > mrpt::mrpt::math::noncentralChi2PDF_CDF	(	unsigned int	degreesOfFreedom,
		double	noncentrality,
		double	arg,
		double	eps = `1e-7`
	)

Returns the 'exact' PDF (first) and CDF (second) of a Non-central chi-squared probability distribution, using an iterative method.

Note: Equivalent to MATLAB's ncx2cdf(arg,degreesOfFreedom,noncentrality)

Definition at line 2066 of file math.cpp.

References ASSERTMSG_, mrpt::obs::gnss::b1, eps, min, noncentralChi2OneIteration(), mrpt::math::sum(), and THROW_EXCEPTION.

Referenced by mrpt::math::chi2PDF(), and TEST().

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◆ normalCDF()

double mrpt::math::normalCDF ( double p )

Evaluates the Gaussian cumulative density function.

The employed approximation is that from W. J. Cody freely available in http://www.netlib.org/specfun/erf

Note: Equivalent to MATLAB normcdf(x,mu,s) with p=(x-mu)/s

Definition at line 1132 of file math.cpp.

References ASSERT_.

Referenced by mrpt::math::normalQuantile(), and TEST().

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◆ normalPDF() [1/3]

double mrpt::math::normalPDF	(	double	x,
		double	mu,
		double	std
	)

Evaluates the univariate normal (Gaussian) distribution at a given point "x".

Definition at line 989 of file math.cpp.

References mrpt::math::square().

Referenced by mrpt::poses::CPose3DQuatPDFGaussian::evaluateNormalizedPDF(), mrpt::poses::CPosePDFGaussian::evaluateNormalizedPDF(), mrpt::poses::CPosePDFGaussianInf::evaluateNormalizedPDF(), mrpt::poses::CPosePDFSOG::evaluateNormalizedPDF(), mrpt::poses::CPose3DQuatPDFGaussian::evaluatePDF(), mrpt::poses::CPosePDFGaussian::evaluatePDF(), mrpt::poses::CPosePDFGaussianInf::evaluatePDF(), mrpt::poses::CPointPDFSOG::evaluatePDF(), mrpt::poses::CPosePDFSOG::evaluatePDF(), mrpt::poses::CPosePDFParticles::evaluatePDF_parzen(), mrpt::obs::CObservation2DRangeScanWithUncertainty::evaluateScanLikelihood(), and TEST().

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◆ normalPDF() [2/3]

template<class VECTORLIKE1 , class VECTORLIKE2 , class MATRIXLIKE >

MATRIXLIKE::Scalar mrpt::math::normalPDF	(	const VECTORLIKE1 &	x,
		const VECTORLIKE2 &	mu,
		const MATRIXLIKE &	cov,
		const bool	scaled_pdf = `false`
	)

inline

Evaluates the multivariate normal (Gaussian) distribution at a given point "x".

Parameters

x	A vector or column or row matrix with the point at which to evaluate the pdf.
mu	A vector or column or row matrix with the Gaussian mean.
cov	The covariance matrix of the Gaussian.
scaled_pdf	If set to true, the PDF will be scaled to be in the range [0,1], in contrast to its integral from [-inf,+inf] being 1.

Definition at line 77 of file distributions.h.

References mrpt::math::cov(), and mrpt::math::normalPDFInf().

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◆ normalPDF() [3/3]

template<typename VECTORLIKE , typename MATRIXLIKE >

MATRIXLIKE::Scalar mrpt::math::normalPDF	(	const VECTORLIKE &	d,
		const MATRIXLIKE &	cov
	)

Evaluates the multivariate normal (Gaussian) distribution at a given point given its distance vector "d" from the Gaussian mean.

Definition at line 88 of file distributions.h.

References ASSERTDEB_, mrpt::math::cov(), M_2PI, MRPT_END, MRPT_START, and mrpt::math::multiply_HCHt_scalar().

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◆ normalPDFInf()

template<class VECTORLIKE1 , class VECTORLIKE2 , class MATRIXLIKE >

MATRIXLIKE::Scalar mrpt::math::normalPDFInf	(	const VECTORLIKE1 &	x,
		const VECTORLIKE2 &	mu,
		const MATRIXLIKE &	cov_inv,
		const bool	scaled_pdf = `false`
	)

inline

Evaluates the multivariate normal (Gaussian) distribution at a given point "x".

Parameters

x	A vector or column or row matrix with the point at which to evaluate the pdf.
mu	A vector or column or row matrix with the Gaussian mean.
cov_inv	The inverse covariance (information) matrix of the Gaussian.
scaled_pdf	If set to true, the PDF will be scaled to be in the range [0,1], in contrast to its integral from [-inf,+inf] being 1.

Definition at line 46 of file distributions.h.

References ASSERTDEB_, M_2PI, MRPT_END, MRPT_START, mrpt::math::multiply_HCHt_scalar(), and mrpt::math::size().

Referenced by mrpt::poses::CPose3DQuatPDFGaussianInf::evaluateNormalizedPDF(), mrpt::poses::CPose3DQuatPDFGaussianInf::evaluatePDF(), and mrpt::math::normalPDF().

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◆ normalQuantile()

double mrpt::math::normalQuantile ( double p )

Evaluates the Gaussian distribution quantile for the probability value p=[0,1].

The employed approximation is that from Peter J. Acklam (pjack.nosp@m.lam@.nosp@m.onlin.nosp@m.e.no), freely available in http://home.online.no/~pjacklam.

Definition at line 1079 of file math.cpp.

References ASSERT_, min, and mrpt::math::normalCDF().

Referenced by mrpt::math::chi2inv().

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◆ productIntegralAndMahalanobisTwoGaussians()

template<typename T , class VECLIKE , class MATLIKE1 , class MATLIKE2 >

void mrpt::math::productIntegralAndMahalanobisTwoGaussians	(	const VECLIKE &	mean_diffs,
		const MATLIKE1 &	COV1,
		const MATLIKE2 &	COV2,
		T &	maha2_out,
		T &	intprod_out,
		const MATLIKE1 *	CROSS_COV12 = `nullptr`
	)

Computes both, the integral of the product of two Gaussians and their square Mahalanobis distance.

See also: productIntegralTwoGaussians, mahalanobisDistance2

Definition at line 183 of file data_utils.h.

References ASSERT_, and M_2PI.

◆ productIntegralTwoGaussians() [1/2]

template<typename T >

T mrpt::math::productIntegralTwoGaussians	(	const std::vector< T > &	mean_diffs,
		const CMatrixTemplateNumeric< T > &	COV1,
		const CMatrixTemplateNumeric< T > &	COV2
	)

Computes the integral of the product of two Gaussians, with means separated by "mean_diffs" and covariances "COV1" and "COV2".

$D = \frac{1}{(2 \pi)^{0.5 N} \sqrt{} } \exp( \Delta_\mu^\top (\Sigma_1 + \Sigma_2 - 2 \Sigma_12)^{-1} \Delta_\mu)$

Definition at line 138 of file data_utils.h.

References ASSERT_, and M_2PI.

◆ productIntegralTwoGaussians() [2/2]

template<typename T , size_t DIM>

T mrpt::math::productIntegralTwoGaussians	(	const std::vector< T > &	mean_diffs,
		const CMatrixFixedNumeric< T, DIM, DIM > &	COV1,
		const CMatrixFixedNumeric< T, DIM, DIM > &	COV2
	)

Computes the integral of the product of two Gaussians, with means separated by "mean_diffs" and covariances "COV1" and "COV2".

$D = \frac{1}{(2 \pi)^{0.5 N} \sqrt{} } \exp( \Delta_\mu^\top (\Sigma_1 + \Sigma_2)^{-1} \Delta_\mu)$

Definition at line 161 of file data_utils.h.

References ASSERT_, M_2PI, and mrpt::math::UNINITIALIZED_MATRIX.

◆ weightedHistogram()

template<class VECTORLIKE1 , class VECTORLIKE2 >

void mrpt::math::weightedHistogram	(	const VECTORLIKE1 &	values,
		const VECTORLIKE1 &	weights,
		float	binWidth,
		VECTORLIKE2 &	out_binCenters,
		VECTORLIKE2 &	out_binValues
	)

Computes the weighted histogram for a vector of values and their corresponding weights.

Parameters

values	[IN] The N values
weights	[IN] The weights for the corresponding N values (don't need to be normalized)
binWidth	[IN] The desired width of the bins
out_binCenters	[OUT] The centers of the M bins generated to cover from the minimum to the maximum value of "values" with the given "binWidth"
out_binValues	[OUT] The ratio of values at each given bin, such as the whole vector sums up the unity.

See also: weightedHistogramLog

Definition at line 408 of file data_utils.h.

References ASSERT_, ASSERTDEB_, mrpt::math::maximum(), mrpt::math::minimum(), MRPT_END, MRPT_START, and mrpt::utils::round().

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◆ weightedHistogramLog()

template<class VECTORLIKE1 , class VECTORLIKE2 >

void mrpt::math::weightedHistogramLog	(	const VECTORLIKE1 &	values,
		const VECTORLIKE1 &	log_weights,
		float	binWidth,
		VECTORLIKE2 &	out_binCenters,
		VECTORLIKE2 &	out_binValues
	)

Computes the weighted histogram for a vector of values and their corresponding log-weights.

Parameters

values	[IN] The N values
weights	[IN] The log-weights for the corresponding N values (don't need to be normalized)
binWidth	[IN] The desired width of the bins
out_binCenters	[OUT] The centers of the M bins generated to cover from the minimum to the maximum value of "values" with the given "binWidth"
out_binValues	[OUT] The ratio of values at each given bin, such as the whole vector sums up the unity.

See also: weightedHistogram

Definition at line 465 of file data_utils.h.

References ASSERT_, ASSERTDEB_, mrpt::math::maximum(), mrpt::math::minimum(), MRPT_END, MRPT_START, and mrpt::utils::round().

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Detailed Description

Functions

Probability density distributions (pdf) distance metrics

Function Documentation

◆ averageLogLikelihood() [1/2]

◆ averageLogLikelihood() [2/2]

◆ averageWrap2Pi()

◆ chi2CDF()

◆ chi2inv()

◆ chi2PDF()

◆ confidenceIntervals()

◆ covariancesAndMean()

◆ covariancesAndMeanWeighted()

◆ KLD_Gaussians()

◆ mahalanobisDistance() [1/3]

◆ mahalanobisDistance() [2/3]

◆ mahalanobisDistance() [3/3]

◆ mahalanobisDistance2() [1/3]

◆ mahalanobisDistance2() [2/3]

◆ mahalanobisDistance2() [3/3]

◆ mahalanobisDistance2AndLogPDF()

◆ mahalanobisDistance2AndPDF()

◆ MATLAB_plotCovariance2D() [1/2]

◆ MATLAB_plotCovariance2D() [2/2]

◆ noncentralChi2CDF()

◆ noncentralChi2PDF_CDF()

◆ normalCDF()

◆ normalPDF() [1/3]

◆ normalPDF() [2/3]

◆ normalPDF() [3/3]

◆ normalPDFInf()

◆ normalQuantile()

◆ productIntegralAndMahalanobisTwoGaussians()

◆ productIntegralTwoGaussians() [1/2]

◆ productIntegralTwoGaussians() [2/2]

◆ weightedHistogram()

◆ weightedHistogramLog()