Instance class for pdf1d_mixture. More...

#include <pdf1d_mixture_sampler.h>

Inheritance diagram for pdf1d_mixture_sampler:

Public Member Functions
	pdf1d_mixture_sampler ()
	Dflt ctor.
	pdf1d_mixture_sampler (const pdf1d_mixture_sampler &)
	Copy ctor.
pdf1d_mixture_sampler &	operator= (const pdf1d_mixture_sampler &)
	Copy operator.
virtual	~pdf1d_mixture_sampler ()
	Destructor.
virtual void	set_model (const pdf1d_pdf &)
	Set model for which this is an instance.
const pdf1d_mixture &	mixture () const
virtual double	sample ()
	Draw random sample from distribution.
virtual void	reseed (unsigned long)
	Reseeds the static random number generator (one per derived class).
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_sampler *	clone () const
	Create a copy on the heap and return base class pointer.
const pdf1d_pdf &	model () const
	PDF of which this is an instance.
virtual void	get_samples (vnl_vector< double > &x)
	Fill x with samples drawn from distribution.
virtual void	regular_samples (vnl_vector< double > &x)
	Fill x with samples possibly chosen so as to represent the distribution.
virtual void	regular_samples_and_prob (vnl_vector< double > &x, vnl_vector< double > &p)
	Fill x with samples possibly chosen so as to represent the distribution.
virtual void	print_summary (vcl_ostream &os) const
	Print class to os.
Protected Attributes
vnl_random	rng_
	The random number generator.
const pdf1d_pdf *	pdf_model_
Private Member Functions
void	init ()
void	delete_stuff ()
Private Attributes
vcl_vector< pdf1d_sampler * >	inst_
	workspace.

Detailed Description

Instance class for pdf1d_mixture.

Implements calculation of prob, gradient, sampling etc

Definition at line 15 of file pdf1d_mixture_sampler.h.

Constructor & Destructor Documentation

pdf1d_mixture_sampler::pdf1d_mixture_sampler ( )

Dflt ctor.

Definition at line 19 of file pdf1d_mixture_sampler.cxx.

pdf1d_mixture_sampler::pdf1d_mixture_sampler ( const pdf1d_mixture_sampler & m )

Copy ctor.

Definition at line 25 of file pdf1d_mixture_sampler.cxx.

pdf1d_mixture_sampler::~pdf1d_mixture_sampler ( ) [virtual]

Destructor.

Definition at line 56 of file pdf1d_mixture_sampler.cxx.

Member Function Documentation

pdf1d_sampler * pdf1d_mixture_sampler::clone ( ) const [virtual]

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

Implements pdf1d_sampler.

Definition at line 140 of file pdf1d_mixture_sampler.cxx.

void pdf1d_mixture_sampler::delete_stuff ( ) [private]

Definition at line 48 of file pdf1d_mixture_sampler.cxx.

void pdf1d_sampler::get_samples ( vnl_vector< double > & x ) [virtual, inherited]

Fill x with samples drawn from distribution.

Definition at line 45 of file pdf1d_sampler.cxx.

void pdf1d_mixture_sampler::init ( ) [private]

Definition at line 15 of file pdf1d_mixture_sampler.cxx.

vcl_string pdf1d_mixture_sampler::is_a ( ) const [virtual]

Name of the class.

Reimplemented from pdf1d_sampler.

Definition at line 119 of file pdf1d_mixture_sampler.cxx.

bool pdf1d_mixture_sampler::is_class ( vcl_string const & s ) const [virtual]

Does the name of the class match the argument?.

Reimplemented from pdf1d_sampler.

Definition at line 126 of file pdf1d_mixture_sampler.cxx.

const pdf1d_mixture & pdf1d_mixture_sampler::mixture ( ) const

Definition at line 63 of file pdf1d_mixture_sampler.cxx.

const pdf1d_pdf & pdf1d_sampler::model ( ) const [inherited]

PDF of which this is an instance.

Definition at line 38 of file pdf1d_sampler.cxx.

pdf1d_mixture_sampler & pdf1d_mixture_sampler::operator= ( const pdf1d_mixture_sampler & m )

Copy operator.

Definition at line 32 of file pdf1d_mixture_sampler.cxx.

void pdf1d_sampler::print_summary ( vcl_ostream & os ) const [virtual, inherited]

Print class to os.

Definition at line 101 of file pdf1d_sampler.cxx.

void pdf1d_sampler::regular_samples ( vnl_vector< double > & x ) [virtual, inherited]

Fill x with samples possibly chosen so as to represent the distribution.

Generate a set of pseudo-random samples, chosen so as to be suitable to represent the distribution. This is meant to be used for estimating continuous integrals with sampled approximations. Where there are multiple peaks (e.g. kernel or mixture models), it is preferred that the number of samples from each component is roughly proportional to the weight for the component. When small numbers are requested, this can be done explicitly. The default is simply to call sample() for each element of x

By default simply randomly sample from distribution

Reimplemented in pdf1d_exponential_sampler, pdf1d_flat_sampler, pdf1d_gaussian_sampler, pdf1d_epanech_kernel_pdf_sampler, pdf1d_gaussian_kernel_pdf_sampler, and pdf1d_weighted_epanech_kernel_sampler.

Definition at line 55 of file pdf1d_sampler.cxx.