Multi-variate Gaussian PDF, with a diagonal covariance matrix. More...

#include <vpdfl_axis_gaussian.h>

Inheritance diagram for vpdfl_axis_gaussian:

Public Member Functions
	vpdfl_axis_gaussian ()
virtual	~vpdfl_axis_gaussian ()
void	set (const vnl_vector< double > &mean, const vnl_vector< double > &var)
double	log_k () const
	Constant offset for log probability.
const vnl_vector< double > &	sd () const
	SD for each dimension.
virtual double	log_p (const vnl_vector< double > &x) const
	Log of probability density at x.
virtual void	gradient (vnl_vector< double > &g, const vnl_vector< double > &x, double &p) const
	Gradient and value of PDF at x.
virtual void	gradient_logp (vnl_vector< double > &g, const vnl_vector< double > &x) const
	Gradient and value of log(p(x)) at x.
virtual vpdfl_sampler_base *	new_sampler () const
	Create a sampler object on the heap.
virtual double	log_prob_thresh (double pass_proportion) const
	Compute threshold for PDF to pass a given proportion.
virtual void	nearest_plausible (vnl_vector< double > &x, double log_p_min) const
	Compute nearest point to x which has a density above a threshold.
short	version_no () const
	Version number for I/O.
virtual vcl_string	is_a () const
	Name of the class.
virtual bool	is_class (vcl_string const &s) const
	Does the name of the class match the argument?.
virtual vpdfl_pdf_base *	clone () const
	Create a copy on the heap and return base class pointer.
virtual void	print_summary (vcl_ostream &os) const
	Print class to os.
virtual void	b_write (vsl_b_ostream &bfs) const
	Save class to binary file stream.
virtual void	b_read (vsl_b_istream &bfs)
	Load class from binary file stream.
const vnl_vector< double > &	mean () const
	Mean of distribution.
const vnl_vector< double > &	variance () const
	Variance of each dimension.
int	n_dims () const
	Number of dimensions.
virtual int	n_peaks () const
	Number of peaks of distribution.
virtual const vnl_vector < double > &	peak (int) const
	Position of the i'th peak.
virtual double	operator() (const vnl_vector< double > &x) const
	Probability density at x.
virtual bool	is_valid_pdf () const
	Return true if the object represents a valid PDF.
Protected Member Functions
void	set_mean (const vnl_vector< double > &m)
void	set_variance (const vnl_vector< double > &v)
Private Member Functions
void	calcLogK ()
void	calcSD ()
double	dx_sigma_dx (const vnl_vector< double > &x) const
	Calculate (x-mu)' * Sigma^-1 * (x-mu).
Private Attributes
double	log_k_
vnl_vector< double >	sd_

Detailed Description

Multi-variate Gaussian PDF, with a diagonal covariance matrix.

Definition at line 23 of file vpdfl_axis_gaussian.h.

Constructor & Destructor Documentation

vpdfl_axis_gaussian::vpdfl_axis_gaussian ( )

Definition at line 27 of file vpdfl_axis_gaussian.cxx.

vpdfl_axis_gaussian::~vpdfl_axis_gaussian ( ) [virtual]

Definition at line 35 of file vpdfl_axis_gaussian.cxx.

Member Function Documentation

void vpdfl_axis_gaussian::b_read ( vsl_b_istream & bfs ) [virtual]

Load class from binary file stream.

Implements vpdfl_pdf_base.

Definition at line 238 of file vpdfl_axis_gaussian.cxx.

void vpdfl_axis_gaussian::b_write ( vsl_b_ostream & bfs ) const [virtual]

Save class to binary file stream.

Implements vpdfl_pdf_base.

Definition at line 230 of file vpdfl_axis_gaussian.cxx.

void vpdfl_axis_gaussian::calcLogK ( ) [private]

Definition at line 41 of file vpdfl_axis_gaussian.cxx.

void vpdfl_axis_gaussian::calcSD ( ) [private]

Definition at line 52 of file vpdfl_axis_gaussian.cxx.

vpdfl_pdf_base * vpdfl_axis_gaussian::clone ( ) const [virtual]

Create a copy on the heap and return base class pointer.

Caller is responsible for deletion

Implements vpdfl_pdf_base.

Definition at line 216 of file vpdfl_axis_gaussian.cxx.

double vpdfl_axis_gaussian::dx_sigma_dx ( const vnl_vector< double > & x ) const [private]

Calculate (x-mu)' * Sigma^-1 * (x-mu).

Mahalanobis distance squared from the mean.

Definition at line 73 of file vpdfl_axis_gaussian.cxx.

void vpdfl_axis_gaussian::gradient	(	vnl_vector< double > &	g,
		const vnl_vector< double > &	x,
		double &	p
	)		const `[virtual]`

Gradient and value of PDF at x.

Computes gradient of PDF at x, and returns the prob at x in p

Implements vpdfl_pdf_base.

Definition at line 104 of file vpdfl_axis_gaussian.cxx.

void vpdfl_axis_gaussian::gradient_logp	(	vnl_vector< double > &	g,
		const vnl_vector< double > &	x
	)		const `[virtual]`

Gradient and value of log(p(x)) at x.

Computes gradient df/dx of f(x)=log(p(x)) at x. Result is vector of same dimensionality as x.

Computes gradient df/dx of f(x)=log(p(x)) at x.

Reimplemented from vpdfl_pdf_base.

Definition at line 134 of file vpdfl_axis_gaussian.cxx.

vcl_string vpdfl_axis_gaussian::is_a ( ) const [virtual]

Name of the class.

Reimplemented from vpdfl_pdf_base.

Definition at line 194 of file vpdfl_axis_gaussian.cxx.

bool vpdfl_axis_gaussian::is_class ( vcl_string const & s ) const [virtual]

Does the name of the class match the argument?.

Reimplemented from vpdfl_pdf_base.

Definition at line 202 of file vpdfl_axis_gaussian.cxx.

bool vpdfl_pdf_base::is_valid_pdf ( ) const [virtual, inherited]

Return true if the object represents a valid PDF.

This will return false, if n_dims() is 0, for example just ofter default construction.

Reimplemented in vpdfl_mixture.

Definition at line 141 of file vpdfl_pdf_base.cxx.

double vpdfl_axis_gaussian::log_k ( ) const [inline]

Constant offset for log probability.

Definition at line 45 of file vpdfl_axis_gaussian.h.

double vpdfl_axis_gaussian::log_p ( const vnl_vector< double > & x ) const [virtual]

Log of probability density at x.

Implements vpdfl_pdf_base.

Definition at line 99 of file vpdfl_axis_gaussian.cxx.

double vpdfl_axis_gaussian::log_prob_thresh ( double pass_proportion ) const [virtual]

Compute threshold for PDF to pass a given proportion.

Reimplemented from vpdfl_pdf_base.

Definition at line 163 of file vpdfl_axis_gaussian.cxx.

const vnl_vector<double>& vpdfl_pdf_base::mean ( ) const [inline, inherited]

Mean of distribution.

Definition at line 46 of file vpdfl_pdf_base.h.

int vpdfl_pdf_base::n_dims ( ) const [inline, inherited]

Number of dimensions.

Definition at line 52 of file vpdfl_pdf_base.h.

virtual int vpdfl_pdf_base::n_peaks ( ) const [inline, virtual, inherited]

Number of peaks of distribution.

Definition at line 55 of file vpdfl_pdf_base.h.

void vpdfl_axis_gaussian::nearest_plausible	(	vnl_vector< double > &	x,
		double	log_p_min
	)		const `[virtual]`

Compute nearest point to x which has a density above a threshold.

If log_p(x)>log_p_min then x unchanged. Otherwise x is moved directly towards the mean (i.e. to the nearest plausible point using a Mahalanobis distance) until log_p(x)=log_p_min.

Parameters: