pdf1d_pdf Class Reference

#include <pdf1d_pdf.h>

Inheritance diagram for pdf1d_pdf:

Detailed Description

Base class for Univariate Probability Density Function classes.

Functions are available to test the plausibility of a vector or set of parameters, to modify a set of parameters so it is plausible and to choose a threshold of plausibility. Also, for cases where the distributions of parameters are multi-modal, the number and centres of each peak can be recorded. This is particularly useful for non-linear and mixture model representations of the parameter distributions.

Definition at line 26 of file pdf1d_pdf.h.

Public Member Functions

pdf1d_pdf ()

Dflt ctor.

virtual ~pdf1d_pdf ()

Destructor.

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 log_p (double x) const =0

Log of probability density at x.

virtual double operator() (double x) const

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 inverse_cdf (double P) const

The inverse cdf.

virtual double gradient (double x, double &p) const =0

Gradient and value of PDF at x.

virtual pdf1d_sampler * new_sampler () const =0

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 double nearest_plausible (double x, double log_p_min) const =0

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

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.

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 =0

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

virtual void print_summary (vcl_ostream &os) const =0

Print class to os.

virtual void b_write (vsl_b_ostream &bfs) const =0

Save class to binary file stream.

virtual void b_read (vsl_b_istream &bfs)=0

Load class from binary file stream.

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 mean_

double var_

Constructor & Destructor Documentation

pdf1d_pdf::pdf1d_pdf ( )

Dflt ctor.

Definition at line 23 of file pdf1d_pdf.cxx.

pdf1d_pdf::~pdf1d_pdf ( ) [virtual]

Destructor.

Definition at line 30 of file pdf1d_pdf.cxx.

Member Function Documentation

void pdf1d_pdf::b_read ( vsl_b_istream & bfs ) [pure virtual]

Load class from binary file stream.

Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_kernel_pdf.
Definition at line 210 of file pdf1d_pdf.cxx.

void pdf1d_pdf::b_write ( vsl_b_ostream & bfs ) const [pure virtual]

Save class to binary file stream.

Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_kernel_pdf.
Definition at line 200 of file pdf1d_pdf.cxx.

double pdf1d_pdf::cdf ( double x ) const [virtual]

Cumulative Probability (P(x'<x) for x' drawn from the distribution.
By default this can be calculated by drawing random samples from the distribution and computing the number less than x.
Reimplemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 44 of file pdf1d_pdf.cxx.

bool pdf1d_pdf::cdf_is_analytic ( ) const [virtual]

Return true if cdf() uses an analytic implementation.
Default is false, as the base implementation is to draw samples from the distribution randomly to estimate cdf(x)
Reimplemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 61 of file pdf1d_pdf.cxx.

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

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

Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.

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

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 131 of file pdf1d_pdf.cxx.

virtual double pdf1d_pdf::gradient ( double x,

double & p

) const [pure virtual]

Gradient and value of PDF at x.
Computes gradient of PDF at x, and returns the prob at x in p
Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.

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

The inverse cdf.
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.
Reimplemented in pdf1d_kernel_pdf.
Definition at line 287 of file pdf1d_pdf.cxx.

vcl_string pdf1d_pdf::is_a ( ) const [virtual]

Name of the class.

Reimplemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_kernel_pdf, pdf1d_mixture, pdf1d_weighted_epanech_kernel_pdf, and pdf1d_weighted_kernel_pdf.
Definition at line 175 of file pdf1d_pdf.cxx.

bool pdf1d_pdf::is_class ( vcl_string const & s ) const [virtual]

Does the name of the class match the argument?.

Reimplemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_kernel_pdf, pdf1d_mixture, pdf1d_weighted_epanech_kernel_pdf, and pdf1d_weighted_kernel_pdf.
Definition at line 183 of file pdf1d_pdf.cxx.

bool pdf1d_pdf::is_valid_pdf ( ) const [virtual]

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 125 of file pdf1d_pdf.cxx.

virtual double pdf1d_pdf::log_p ( double x ) const [pure virtual]

Log of probability density at x.

Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.

double pdf1d_pdf::log_prob_thresh ( double pass_proportion ) const [virtual]

Compute threshold for PDF to pass a given proportion.

Reimplemented in pdf1d_exponential, pdf1d_flat, and pdf1d_gaussian.
Definition at line 67 of file pdf1d_pdf.cxx.

double pdf1d_pdf::mean ( ) const [inline]

Mean of distribution.

Definition at line 42 of file pdf1d_pdf.h.

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

Number of peaks of distribution.

Definition at line 48 of file pdf1d_pdf.h.

virtual double pdf1d_pdf::nearest_plausible ( double x,

double log_p_min

) const [pure 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 move (typically up the gradient) until log_p(x)>=log_p_min.
Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.

virtual pdf1d_sampler* pdf1d_pdf::new_sampler ( ) const [pure virtual]

Create a sampler object on the heap.
Caller is responsible for deletion.
Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.

double pdf1d_pdf::operator() ( double x ) const [virtual]

Probability density at x.

Reimplemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 36 of file pdf1d_pdf.cxx.

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

Position of the i'th peak.

Definition at line 51 of file pdf1d_pdf.h.

void pdf1d_pdf::print_summary ( vcl_ostream & os ) const [pure virtual]

Print class to os.

Implemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_kernel_pdf, pdf1d_mixture, and pdf1d_weighted_kernel_pdf.
Definition at line 191 of file pdf1d_pdf.cxx.

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

Reimplemented in pdf1d_gaussian.
Definition at line 31 of file pdf1d_pdf.h.

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

Definition at line 32 of file pdf1d_pdf.h.

double pdf1d_pdf::variance ( ) const [inline]

Variance of each dimension.

Definition at line 45 of file pdf1d_pdf.h.

short pdf1d_pdf::version_no ( ) const

Version number for I/O.

Reimplemented in pdf1d_epanech_kernel_pdf, pdf1d_exponential, pdf1d_flat, pdf1d_gaussian, pdf1d_gaussian_kernel_pdf, pdf1d_kernel_pdf, pdf1d_mixture, pdf1d_weighted_epanech_kernel_pdf, and pdf1d_weighted_kernel_pdf.
Definition at line 161 of file pdf1d_pdf.cxx.

bool pdf1d_pdf::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.
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 141 of file pdf1d_pdf.cxx.

Member Data Documentation

double pdf1d_pdf::mean_ [private]

Definition at line 28 of file pdf1d_pdf.h.

double pdf1d_pdf::var_ [private]

Definition at line 29 of file pdf1d_pdf.h.

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

contrib/mul/pdf1d/pdf1d_pdf.h
contrib/mul/pdf1d/pdf1d_pdf.cxx

Generated on Thu Jan 10 14:43:23 2008 for contrib/mul/pdf1d by

1.4.4


Public Member Functions
	pdf1d_pdf ()
	Dflt ctor.
virtual	~pdf1d_pdf ()
	Destructor.
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	log_p (double x) const =0
	Log of probability density at x.
virtual double	operator() (double x) const
	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	inverse_cdf (double P) const
	The inverse cdf.
virtual double	gradient (double x, double &p) const =0
	Gradient and value of PDF at x.
virtual pdf1d_sampler *	new_sampler () const =0
	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 double	nearest_plausible (double x, double log_p_min) const =0
	Compute nearest point to x which has a density above a threshold.
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.
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 =0
	Create a copy on the heap and return base class pointer.
virtual void	print_summary (vcl_ostream &os) const =0
	Print class to os.
virtual void	b_write (vsl_b_ostream &bfs) const =0
	Save class to binary file stream.
virtual void	b_read (vsl_b_istream &bfs)=0
	Load class from binary file stream.
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	mean_
double	var_