Class for exponential distributions. More...

#include <pdf1d_exponential.h>

Inheritance diagram for pdf1d_exponential:

Public Member Functions
	pdf1d_exponential ()
	Dflt ctor (creates exponential distribution in range [0,1]).
	pdf1d_exponential (double lambda)
	Dflt ctor (creates exponential distribution in range [lo,hi]).
virtual	~pdf1d_exponential ()
	Destructor.
double	sd () const
	Return standard deviation.
void	set_lambda (double lambda)
	Creates exponential distribution with given lambda.
double	lambda () const
	Value of lambda.
virtual pdf1d_sampler *	new_sampler () const
	Create a sampler object on the heap.
virtual double	operator() (double x) const
	Probability density at x.
virtual double	log_p (double x) const
	Log of probability density at x.
virtual double	cdf (double x) const
	Cumulative Probability (P(x'<x) for x' drawn from the distribution).
virtual bool	cdf_is_analytic () const
	Return true if cdf() uses an analytic implementation.
virtual double	gradient (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 double	nearest_plausible (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 pdf1d_pdf *	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.
double	mean () const
	Mean of distribution.
double	variance () const
	Variance of each dimension.
virtual int	n_peaks () const
	Number of peaks of distribution.
virtual double	peak (int) const
	Position of the i'th peak.
virtual double	inverse_cdf (double P) const
	The inverse cdf.
virtual bool	is_valid_pdf () const
	Return true if the object represents a valid PDF.
void	get_samples (vnl_vector< double > &x) const
	Fill x with samples drawn from distribution.
bool	write_plot_file (const vcl_string &plot_file, double min_x, double max_x, int n) const
	Write values (x,p(x)) to text file suitable for plotting.
Protected Member Functions
void	set_mean (double m)
void	set_variance (double v)
Private Attributes
double	lambda_
double	log_lambda_

Detailed Description

Class for exponential distributions.

$p(x)=\lambda exp(-\lambda x)$

Definition at line 15 of file pdf1d_exponential.h.

Constructor & Destructor Documentation

pdf1d_exponential::pdf1d_exponential ( )

Dflt ctor (creates exponential distribution in range [0,1]).

Definition at line 19 of file pdf1d_exponential.cxx.

pdf1d_exponential::pdf1d_exponential ( double lambda )

Dflt ctor (creates exponential distribution in range [lo,hi]).

Definition at line 24 of file pdf1d_exponential.cxx.

pdf1d_exponential::~pdf1d_exponential ( ) [virtual]

Destructor.

Definition at line 31 of file pdf1d_exponential.cxx.

Member Function Documentation

void pdf1d_exponential::b_read ( vsl_b_istream & bfs ) [virtual]

Load class from binary file stream.

Implements pdf1d_pdf.

Definition at line 187 of file pdf1d_exponential.cxx.

void pdf1d_exponential::b_write ( vsl_b_ostream & bfs ) const [virtual]

Save class to binary file stream.

Implements pdf1d_pdf.

Definition at line 176 of file pdf1d_exponential.cxx.

double pdf1d_exponential::cdf ( double x ) const [virtual]

Cumulative Probability (P(x'<x) for x' drawn from the distribution).

Returns 1-exp(-x/lambda)

Reimplemented from pdf1d_pdf.

Definition at line 75 of file pdf1d_exponential.cxx.

bool pdf1d_exponential::cdf_is_analytic ( ) const [virtual]

Return true if cdf() uses an analytic implementation.

Reimplemented from pdf1d_pdf.

Definition at line 81 of file pdf1d_exponential.cxx.

pdf1d_pdf * pdf1d_exponential::clone ( ) const [virtual]

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

Implements pdf1d_pdf.

Definition at line 157 of file pdf1d_exponential.cxx.

void pdf1d_pdf::get_samples ( vnl_vector< double > & x ) const [inherited]

Fill x with samples drawn from distribution.

Utility function. This calls new_sampler() to do the work, then deletes the sampler again. If you intend calling this repeatedly, create a sampler yourself.

Definition at line 132 of file pdf1d_pdf.cxx.

double pdf1d_exponential::gradient	(	double	x,
		double &	p
	)		const `[virtual]`

Gradient of PDF at x.

Implements pdf1d_pdf.

Definition at line 89 of file pdf1d_exponential.cxx.

double pdf1d_pdf::inverse_cdf ( double P ) const [virtual, inherited]

The inverse cdf.

The inverse cumulative distribution function.

The value of x: P(x'<x) = P for x' drawn from distribution pdf. The default version of this algorithm uses sampling if !cdf_is_analytic(), and Newton-Raphson root finding otherwise.

The value of x: P(x'<x) = P for x' drawn from distribution pdf.

Reimplemented in pdf1d_kernel_pdf.

Definition at line 288 of file pdf1d_pdf.cxx.

vcl_string pdf1d_exponential::is_a ( ) const [virtual]

Name of the class.

Reimplemented from pdf1d_pdf.

Definition at line 129 of file pdf1d_exponential.cxx.

bool pdf1d_exponential::is_class ( vcl_string const & s ) const [virtual]

Does the name of the class match the argument?.

Reimplemented from pdf1d_pdf.

Definition at line 139 of file pdf1d_exponential.cxx.

bool pdf1d_pdf::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 pdf1d_mixture.

Definition at line 126 of file pdf1d_pdf.cxx.

double pdf1d_exponential::lambda ( ) const [inline]

Value of lambda.

Definition at line 37 of file pdf1d_exponential.h.

double pdf1d_exponential::log_p ( 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 pdf1d_pdf.

Definition at line 68 of file pdf1d_exponential.cxx.

double pdf1d_exponential::log_prob_thresh ( double pass_proportion ) const [virtual]

Compute threshold for PDF to pass a given proportion.

Reimplemented from pdf1d_pdf.

Definition at line 105 of file pdf1d_exponential.cxx.

double pdf1d_pdf::mean ( ) const [inline, inherited]

Mean of distribution.

Definition at line 42 of file pdf1d_pdf.h.

virtual int pdf1d_pdf::n_peaks ( ) const [inline, virtual, inherited]

Number of peaks of distribution.

Definition at line 48 of file pdf1d_pdf.h.

double pdf1d_exponential::nearest_plausible	(	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 returned unchanged. Otherwise x is moved (typically up the gradient) 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 pdf1d_pdf.

Definition at line 117 of file pdf1d_exponential.cxx.

pdf1d_sampler * pdf1d_exponential::new_sampler ( ) const [virtual]

Create a sampler object on the heap.

Caller is responsible for deletion.

Implements pdf1d_pdf.

Definition at line 51 of file pdf1d_exponential.cxx.

double pdf1d_exponential::operator() ( double x ) const [virtual]

Probability density at x.

Reimplemented from pdf1d_pdf.

Definition at line 60 of file pdf1d_exponential.cxx.

virtual double pdf1d_pdf::peak ( int ) const [inline, virtual, inherited]

Position of the i'th peak.

Definition at line 51 of file pdf1d_pdf.h.

void pdf1d_exponential::print_summary ( vcl_ostream & os ) const [virtual]

Print class to os.

Implements pdf1d_pdf.

Definition at line 167 of file pdf1d_exponential.cxx.

double pdf1d_exponential::sd ( ) const [inline]

Return standard deviation.

Definition at line 31 of file pdf1d_exponential.h.

void pdf1d_exponential::set_lambda ( double lambda )

Creates exponential distribution with given lambda.

Initialise.

Definition at line 38 of file pdf1d_exponential.cxx.

void pdf1d_pdf::set_mean ( double m ) [inline, protected, inherited]

Reimplemented in pdf1d_gaussian.

Definition at line 31 of file pdf1d_pdf.h.

void pdf1d_pdf::set_variance ( double v ) [inline, protected, inherited]

Definition at line 32 of file pdf1d_pdf.h.

double pdf1d_pdf::variance ( ) const [inline, inherited]

Variance of each dimension.

Definition at line 45 of file pdf1d_pdf.h.

short pdf1d_exponential::version_no ( ) const

Version number for I/O.

Reimplemented from pdf1d_pdf.

Definition at line 148 of file pdf1d_exponential.cxx.

bool pdf1d_pdf::write_plot_file	(	const vcl_string &	plot_file,
		double	min_x,
		double	max_x,
		int	n
	)		const `[inherited]`

Write values (x,p(x)) to text file suitable for plotting.

Evaluate pdf at n points in range [min_x,max_x] and write a text file, each line of which is {x p(x)}, suitable for plotting with many graph packages

Definition at line 142 of file pdf1d_pdf.cxx.

Member Data Documentation

double pdf1d_exponential::lambda_ [private]

Definition at line 17 of file pdf1d_exponential.h.

double pdf1d_exponential::log_lambda_ [private]

Definition at line 18 of file pdf1d_exponential.h.

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

contrib/mul/pdf1d/pdf1d_exponential.h
contrib/mul/pdf1d/pdf1d_exponential.cxx

Public Member Functions

Protected Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation