Class for multi-variate gaussians with arbitrary axes. More...

#include <vpdfl_gaussian.h>

Inheritance diagram for vpdfl_gaussian:

Public Member Functions
	vpdfl_gaussian ()
	Dflt ctor.
virtual	~vpdfl_gaussian ()
	Destructor.
void	set (const vnl_vector< double > &mean, const vnl_vector< double > &variance, const vnl_matrix< double > &evecs, const vnl_vector< double > &evals)
	Initialise.
virtual void	set (const vnl_vector< double > &mean, const vnl_matrix< double > &evecs, const vnl_vector< double > &evals)
	Initialise safely.
void	set (const vnl_vector< double > &mean, const vnl_matrix< double > &covar, double min_eval=1e-6)
	Initialise from mean and covariance matrix.
void	set_mean (const vnl_vector< double > &mean)
	Modify just the mean of the distribution.
const vnl_matrix< double > &	eigenvecs () const
	Eigenvectors of covariance matrix.
const vnl_vector< double > &	eigenvals () const
	Eigenvalues of covariance matrix.
vnl_matrix< double >	covariance () const
	The Covariance matrix of the Gaussian.
double	log_k () const
	log of normalisation constant for gaussian.
virtual vpdfl_sampler_base *	new_sampler () const
	Create a sampler object on the heap.
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 of PDF at x.
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 void	gradient_logp (vnl_vector< double > &g, const vnl_vector< double > &x) const
	Gradient and value of log(p(x)) at x.
virtual bool	is_valid_pdf () const
	Return true if the object represents a valid PDF.
Protected Member Functions
void	set_variance (const vnl_vector< double > &v)
Protected Attributes
vnl_vector< double >	dx_
	Workspace.
vnl_vector< double >	b_
	Workspace.
Private Member Functions
void	calcLogK ()
double	dx_sigma_dx (const vnl_vector< double > &x) const
	Calculate (x-mu)' * Sigma^-1 * (x-mu).
Private Attributes
vnl_matrix< double >	evecs_
vnl_vector< double >	evals_
double	log_k_

Detailed Description

Class for multi-variate gaussians with arbitrary axes.

Covariance matrix is represented by its eigenvectors and values

Definition at line 24 of file vpdfl_gaussian.h.

Constructor & Destructor Documentation

vpdfl_gaussian::vpdfl_gaussian ( )

Dflt ctor.

Definition at line 70 of file vpdfl_gaussian.cxx.

vpdfl_gaussian::~vpdfl_gaussian ( ) [virtual]

Destructor.

Definition at line 76 of file vpdfl_gaussian.cxx.

Member Function Documentation

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

Load class from binary file stream.

Implements vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 414 of file vpdfl_gaussian.cxx.

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

Save class to binary file stream.

Implements vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 400 of file vpdfl_gaussian.cxx.

void vpdfl_gaussian::calcLogK ( ) [private]

Definition at line 82 of file vpdfl_gaussian.cxx.

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

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

Implements vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 340 of file vpdfl_gaussian.cxx.

vnl_matrix< double > vpdfl_gaussian::covariance ( ) const

The Covariance matrix of the Gaussian.

This value is calculated on the fly each time so calling this function may not be very efficient

Definition at line 180 of file vpdfl_gaussian.cxx.

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

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

This is the Mahalanobis distance squared from the mean.

Definition at line 200 of file vpdfl_gaussian.cxx.

const vnl_vector<double>& vpdfl_gaussian::eigenvals ( ) const [inline]

Eigenvalues of covariance matrix.

The list is ordered - largest Eigenvalues first.

Definition at line 89 of file vpdfl_gaussian.h.

const vnl_matrix<double>& vpdfl_gaussian::eigenvecs ( ) const [inline]

Eigenvectors of covariance matrix.

List ordering corresponds to eVals();

Definition at line 85 of file vpdfl_gaussian.h.

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

Gradient of PDF at x.

Implements vpdfl_pdf_base.

Definition at line 242 of file vpdfl_gaussian.cxx.

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

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

Gradient 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. Default baseclass implementation uses gradient() to compute grad/p

Computes gradient df/dx of f(x)=log(p(x)) at x. Default baseclass implementation uses gradient() to compute grad/p

Reimplemented in vpdfl_axis_gaussian.

Definition at line 127 of file vpdfl_pdf_base.cxx.

vcl_string vpdfl_gaussian::is_a ( ) const [virtual]

Name of the class.

Reimplemented from vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 312 of file vpdfl_gaussian.cxx.

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

Does the name of the class match the argument?.

Reimplemented from vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 322 of file vpdfl_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_gaussian::log_k ( ) const [inline]

log of normalisation constant for gaussian.

Definition at line 97 of file vpdfl_gaussian.h.

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

Log of probability density at x.

This value is also the Normalised Mahalanobis distance from the centroid to the given vector.

Implements vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 235 of file vpdfl_gaussian.cxx.

double vpdfl_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 281 of file vpdfl_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_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:

x	This may be modified to the nearest plausible position.
log_p_min	lower threshold for log_p(x)

Implements vpdfl_pdf_base.

Definition at line 290 of file vpdfl_gaussian.cxx.

vpdfl_sampler_base * vpdfl_gaussian::new_sampler ( ) const [virtual]

Create a sampler object on the heap.

Caller is responsible for deletion.

Implements vpdfl_pdf_base.

Definition at line 189 of file vpdfl_gaussian.cxx.

double vpdfl_pdf_base::operator() ( const vnl_vector< double > & x ) const [virtual, inherited]

Probability density at x.

Reimplemented in vpdfl_mixture, and vpdfl_gaussian_kernel_pdf.

Definition at line 34 of file vpdfl_pdf_base.cxx.

virtual const vnl_vector<double>& vpdfl_pdf_base::peak ( int ) const [inline, virtual, inherited]

Position of the i'th peak.

Definition at line 58 of file vpdfl_pdf_base.h.

void vpdfl_gaussian::print_summary ( vcl_ostream & os ) const [virtual]

Print class to os.

Implements vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 384 of file vpdfl_gaussian.cxx.

void vpdfl_gaussian::set	(	const vnl_vector< double > &	mean,
		const vnl_vector< double > &	variance,
		const vnl_matrix< double > &	evecs,
		const vnl_vector< double > &	evals
	)

Initialise.

WARNING - the error checking for inconsistent parameters is not foolproof.

Definition at line 128 of file vpdfl_gaussian.cxx.

void vpdfl_gaussian::set	(	const vnl_vector< double > &	mean,
		const vnl_matrix< double > &	evecs,
		const vnl_vector< double > &	evals
	)		`[virtual]`

Initialise safely.

Calculates the variance, and checks that the Eigenvalues are ordered and the Eigenvectors are unit normal

Turn off assertions to remove error checking.

This functions should only be used by builders.

Calculates the variance, and checks that the Eigenvalues are ordered and the Eigenvectors are unit normal Turn off assertions to remove error checking.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 96 of file vpdfl_gaussian.cxx.

void vpdfl_gaussian::set	(	const vnl_vector< double > &	mean,
		const vnl_matrix< double > &	covar,
		double	min_eval = `1e-6`
	)

Initialise from mean and covariance matrix.

Note, eigenvectors computed from covar, and those corresponding to evals smaller than min_eval are truncated

This functions should only be used by builders.

Note, eigenvectors computed from covar, and those corresponding to evals smaller than min_eval are truncated

Definition at line 154 of file vpdfl_gaussian.cxx.

void vpdfl_gaussian::set_mean ( const vnl_vector< double > & mean )

Modify just the mean of the distribution.

This functions should only be used by builders.

Reimplemented from vpdfl_pdf_base.

Definition at line 144 of file vpdfl_gaussian.cxx.

void vpdfl_pdf_base::set_variance ( const vnl_vector< double > & v ) [inline, protected, inherited]

Definition at line 36 of file vpdfl_pdf_base.h.

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

Variance of each dimension.

Definition at line 49 of file vpdfl_pdf_base.h.

short vpdfl_gaussian::version_no ( ) const

Version number for I/O.

Reimplemented from vpdfl_pdf_base.

Reimplemented in vpdfl_pc_gaussian.

Definition at line 331 of file vpdfl_gaussian.cxx.

Member Data Documentation

vnl_vector<double> vpdfl_gaussian::b_ [mutable, protected]

Workspace.

Usually the input vector after normalisation.

Definition at line 41 of file vpdfl_gaussian.h.

vnl_vector<double> vpdfl_gaussian::dx_ [mutable, protected]

Workspace.

The difference between an input vector and the mean

Definition at line 38 of file vpdfl_gaussian.h.

vnl_vector<double> vpdfl_gaussian::evals_ [private]

Definition at line 27 of file vpdfl_gaussian.h.

vnl_matrix<double> vpdfl_gaussian::evecs_ [private]

Definition at line 26 of file vpdfl_gaussian.h.

double vpdfl_gaussian::log_k_ [private]

Definition at line 28 of file vpdfl_gaussian.h.

The documentation for this class was generated from the following files:

contrib/mul/vpdfl/vpdfl_gaussian.h
contrib/mul/vpdfl/vpdfl_gaussian.cxx

Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation