bdm::merger_base Class Reference

Base class for general combination of pdfs on discrete support. More...

#include <merger.h>

List of all members.

Public Member Functions

virtual string to_string ()

This method returns a basic info about the current instance.

virtual void to_setting (Setting &set) const

This method save all the instance properties into the Setting structure.

Constructors

merger_base ()

Default constructor.

merger_base (const Array< shared_ptr< mpdf > > &S)

Constructor from sources.

void set_sources (const Array< shared_ptr< mpdf > > &Sources)

Function setting the main internal structures.

void set_support (rectangular_support &Sup)

Set support points from rectangular grid.

void set_support (discrete_support &Sup)

Set support points from dicrete grid.

void set_debug_file (const string fname)

set debug file

void set_method (MERGER_METHOD MTH=DFLT_METHOD, double beta0=DFLT_beta)

Set internal parameters used in approximation.

void set_support (const epdf &overall, int N)

Set support points from a pdf by drawing N samples.

virtual ~merger_base ()

Destructor.

Mathematical operations

virtual void merge ()

Merge given sources in given points.

vec merge_points (mat &lW)

Merge log-likelihood values in points using method specified by parameter METHOD.

vec mean () const

weight w is a

mat covariance () const

vec variance () const

return expected variance (not covariance!)

Access to attributes

eEmp & _Smp ()

Access function.

void from_setting (const Setting &set)

load from setting

void validate ()

This method TODO.

Constructors

Construction of each epdf should support two types of constructors:

empty constructor,

copy constructor,

The following constructors should be supported for convenience:

constructor followed by calling set_parameters()

constructor accepting random variables calling set_rv()

All internal data structures are constructed as empty. Their values (including sizes) will be set by method set_parameters(). This way references can be initialized in constructors.

void set_parameters (int dim0)

Matematical Operations

virtual vec sample () const

Returns a sample, $ x $ from density $ f_x()$ .

virtual mat sample_m (int N) const

Returns N samples, $[x_1 , x_2 , \ldots \$ from density $ f_x(rv)$ .

virtual double evallog (const vec &val) const

Compute log-probability of argument val In case the argument is out of suport return -Infinity.

virtual vec evallog_m (const mat &Val) const

Compute log-probability of multiple values argument val.

virtual vec evallog_m (const Array< vec > &Avec) const

Compute log-probability of multiple values argument val.

virtual shared_ptr< mpdf > condition (const RV &rv) const

Return conditional density on the given RV, the remaining rvs will be in conditioning.

virtual shared_ptr< epdf > marginal (const RV &rv) const

Return marginal density on the given RV, the remainig rvs are intergrated out.

virtual void qbounds (vec &lb, vec &ub, double percentage=0.95) const

Lower and upper bounds of percentage % quantile, returns mean-2*sigma as default.

Connection to other classes

Description of the random quantity via attribute rv is optional. For operations such as sampling rv does not need to be set. However, for marginalization and conditioning rv has to be set. NB:

void set_rv (const RV &rv0)

Name its rv.

bool isnamed () const

True if rv is assigned.

const RV & _rv () const

Return name (fails when isnamed is false).

Access to attributes

int dimension () const

Size of the random variable.

Protected Attributes

Array< shared_ptr< mpdf > > mpdfs

Elements of composition.

Array< datalink_m2e * > dls

Data link for each mpdf in mpdfs.

Array< RV > rvzs

Array of rvs that are not modelled by mpdfs at all, $ z_i $ .

Array< datalink_m2e * > zdls

Data Links for extension $ f(z_i|x_i,y_i) $ .

int Npoints

number of support points

int Nsources

number of sources

MERGER_METHOD METHOD

switch of the methoh used for merging

double beta

Prior on the log-normal merging model.

eEmp eSmp

Projection to empirical density (could also be piece-wise linear).

bool DBG

debug or not debug

it_file * dbg_file

debugging file

int dim

dimension of the random variable

RV rv

Description of the random variable.

Static Protected Attributes

static const MERGER_METHOD DFLT_METHOD = LOGNORMAL

Default for METHOD.

static const double DFLT_beta = 1.2

default for beta

Detailed Description

Base class for general combination of pdfs on discrete support.

Mixtures of Gaussian densities are used internally. Switching to other densities should be trivial.

The merged pdfs are expected to be of the form:

$f(x_i|y_i), i=1..n$

where the resulting merger is a density on $\cup [x_i,y_i]$ . Note that all variables will be joined.

As a result of this feature, each source must be extended to common support

$f(z_i|y_i,x_i) f(x_i|y_i) f(y_i) i=1..n$

where $ z_i $ accumulate variables that were not in the original source. These extensions are calculated on-the-fly.

However, these operations can not be performed in general. Hence, this class merges only sources on common support, $y_i={}, z_i={}, \forall i$ . For merging of more general cases, use offsprings merger_mix and merger_grid.

Member Function Documentation

vec bdm::merger_base::mean ( ) const [inline, virtual]

weight w is a

sample from merged density

Reimplemented from bdm::epdf.

References bdm::eEmp::_samples(), bdm::eEmp::_w(), bdm::epdf::dim, eSmp, and Npoints.

Referenced by variance().

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

merger.h
merger.cpp


Public Member Functions
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	to_setting (Setting &set) const
	This method save all the instance properties into the Setting structure.
Constructors
	merger_base ()
	Default constructor.
	merger_base (const Array< shared_ptr< mpdf > > &S)
	Constructor from sources.
void	set_sources (const Array< shared_ptr< mpdf > > &Sources)
	Function setting the main internal structures.
void	set_support (rectangular_support &Sup)
	Set support points from rectangular grid.
void	set_support (discrete_support &Sup)
	Set support points from dicrete grid.
void	set_debug_file (const string fname)
	set debug file
void	set_method (MERGER_METHOD MTH=DFLT_METHOD, double beta0=DFLT_beta)
	Set internal parameters used in approximation.
void	set_support (const epdf &overall, int N)
	Set support points from a pdf by drawing N samples.
virtual	~merger_base ()
	Destructor.
Mathematical operations
virtual void	merge ()
	Merge given sources in given points.
vec	merge_points (mat &lW)
	Merge log-likelihood values in points using method specified by parameter METHOD.
vec	mean () const
	weight w is a
mat	covariance () const
vec	variance () const
	return expected variance (not covariance!)
Access to attributes
eEmp &	_Smp ()
	Access function.
void	from_setting (const Setting &set)
	load from setting
void	validate ()
	This method TODO.
Constructors
Construction of each epdf should support two types of constructors: empty constructor, copy constructor, The following constructors should be supported for convenience: constructor followed by calling `set_parameters()` constructor accepting random variables calling `set_rv()` All internal data structures are constructed as empty. Their values (including sizes) will be set by method `set_parameters()`. This way references can be initialized in constructors.
void	set_parameters (int dim0)
Matematical Operations
virtual vec	sample () const
	Returns a sample, from density .
virtual mat	sample_m (int N) const
	Returns N samples, $[x_1 , x_2 , \ldots \$ from density .
virtual double	evallog (const vec &val) const
	Compute log-probability of argument `val` In case the argument is out of suport return -Infinity.
virtual vec	evallog_m (const mat &Val) const
	Compute log-probability of multiple values argument `val`.
virtual vec	evallog_m (const Array< vec > &Avec) const
	Compute log-probability of multiple values argument `val`.
virtual shared_ptr< mpdf >	condition (const RV &rv) const
	Return conditional density on the given RV, the remaining rvs will be in conditioning.
virtual shared_ptr< epdf >	marginal (const RV &rv) const
	Return marginal density on the given RV, the remainig rvs are intergrated out.
virtual void	qbounds (vec &lb, vec &ub, double percentage=0.95) const
	Lower and upper bounds of `percentage` % quantile, returns mean-2*sigma as default.
Connection to other classes
Description of the random quantity via attribute `rv` is optional. For operations such as sampling `rv` does not need to be set. However, for `marginalization` and `conditioning` `rv` has to be set. NB:
void	set_rv (const RV &rv0)
	Name its rv.
bool	isnamed () const
	True if rv is assigned.
const RV &	_rv () const
	Return name (fails when isnamed is false).
Access to attributes
int	dimension () const
	Size of the random variable.
Protected Attributes
Array< shared_ptr< mpdf > >	mpdfs
	Elements of composition.
Array< datalink_m2e * >	dls
	Data link for each mpdf in mpdfs.
Array< RV >	rvzs
	Array of rvs that are not modelled by mpdfs at all, .
Array< datalink_m2e * >	zdls
	Data Links for extension .
int	Npoints
	number of support points
int	Nsources
	number of sources
MERGER_METHOD	METHOD
	switch of the methoh used for merging
double	beta
	Prior on the log-normal merging model.
eEmp	eSmp
	Projection to empirical density (could also be piece-wise linear).
bool	DBG
	debug or not debug
it_file *	dbg_file
	debugging file
int	dim
	dimension of the random variable
RV	rv
	Description of the random variable.
Static Protected Attributes
static const MERGER_METHOD	DFLT_METHOD = LOGNORMAL
	Default for METHOD.
static const double	DFLT_beta = 1.2
	default for beta