kjb::Abstract_dynamics Class Reference

#include <abstract_dynamics.h>

Inheritance diagram for kjb::Abstract_dynamics:

Public Member Functions
	Abstract_dynamics (double ialpha=0.99, unsigned int ikick=0)
	Abstract_dynamics (kjb::Vector iparameters, kjb::Vector ideltas, double ialpha=0.99, unsigned int ikick=0)
	Abstract_dynamics (const Abstract_dynamics &src)
Abstract_dynamics &	operator= (const Abstract_dynamics &src)
virtual	~Abstract_dynamics ()
void	set_num_parameters (unsigned int num_params)
	Sets the number of parameters we will be sampling over. More...

Protected Member Functions
void	run (unsigned int iterations)
	Implements stochastic dynamics sampling on a set of parameters. This implementation follows the algorithm described in the 1993 Neal Paper, with leapfrog discretization and stochastic transitions weighted by the stochastic alpha parameter. This function MUST be kept protected so that only classes that inherit from this class can use it. Each of this derived class will have to provide its own public interface to this method. More...
virtual void	compute_energy_gradient ()=0
virtual void	log_sample ()

Protected Attributes
kjb::Vector	parameters
	The parameters to sample over. More...
kjb::Vector	deltas
	The size of the step to take in the gradient direction. This step size is different for each parameter we are sampling over. More...
kjb::Vector	gradients
kjb::Vector	stochastic_momenta
kjb::Vector	momenta
kjb::Vector	temp_momenta
kjb::Vector	stochastic_transition
kjb::MV_gaussian_distribution *	mv_gauss
double	alpha
double	st_alpha
unsigned int	kick

Detailed Description

dynamics sampling on a set of parameters.This implementation follows the algorithm described in the 1993 Neal Paper, with leapfrog discretization and stochastic transitions weighted by the stochastic alpha parameter.

Constructor & Destructor Documentation

kjb::Abstract_dynamics::Abstract_dynamics ( double  ialpha = 0.99, unsigned int  ikick = 0  )

inline

Parameters

ialpha	This parameter tweaks the contribution of the stochastic transition. If it is close to one, the random transition has little weight, and the dynamics mostly follow the momenta. The closer it gets to zero, the more the stochastic transitions will influence the selected trajectory.
ikick	This parameters is used to reset the momenta to the correct trajectory, thus eliminating the effect of the stochastic transitions. It specifies after how many transitions the momenta are to be reset. This was not fully tested

kjb::Abstract_dynamics::Abstract_dynamics ( kjb::Vector  iparameters, kjb::Vector  ideltas, double  ialpha = 0.99, unsigned int  ikick = 0  )

inline

Parameters

iparameters	The initial values of the parameters to sample over
ideltas	The size of the step to take in the gradient direction. This step size is different for each parameter we are sampling over
ialpha	This parameter tweaks the contribution of the stochastic transition. If it is close to one, the random transition has little weight, and the dynamics mostly follow the momenta. The closer it gets to zero, the more the stochastic transitions will influence the selected trajectory.
ikick	this parameters is used to reset the momenta to the correct trajectory, thus eliminating the effect of the stochastic transitions. It specifies after how many transitions the momenta are to be reset. This was not fully tested

Abstract_dynamics::Abstract_dynamics

(

const Abstract_dynamics &

src

)

Parameters

src	The Abstract_dynamics to copy into this one

virtual kjb::Abstract_dynamics::~Abstract_dynamics ( )

inlinevirtual

Member Function Documentation

virtual void kjb::Abstract_dynamics::compute_energy_gradient ( )

protectedpure virtual

Implemented in kjb::Likelihood_dynamics.

virtual void kjb::Abstract_dynamics::log_sample ( )

inlineprotectedvirtual

Reimplemented in kjb::Likelihood_dynamics.

Abstract_dynamics & Abstract_dynamics::operator=

(

const Abstract_dynamics &

src

)

Parameters

src	The Abstract_dynamics to assign to this one

void Abstract_dynamics::run ( unsigned int  iterations )

protected

Implements stochastic dynamics sampling on a set of parameters. This implementation follows the algorithm described in the 1993 Neal Paper, with leapfrog discretization and stochastic transitions weighted by the stochastic alpha parameter. This function MUST be kept protected so that only classes that inherit from this class can use it. Each of this derived class will have to provide its own public interface to this method.

Implements stochastic dynamics sampling on a set of parameters. This implementation follows the algorithm described in the 1993 Neal Paper, with leapfrog discretization and stochastic transitions weighted by the stochastic alpha parameter.

Parameters

iterations

The number of iterations

void Abstract_dynamics::set_num_parameters

(

unsigned int

num_params

)

Sets the number of parameters we will be sampling over.

Sets the number of parameters we are sampling over and prepares the data structures needed for the computation accordingly

Parameters

num_params

the number of parameters

Member Data Documentation

double kjb::Abstract_dynamics::alpha

protected

kjb::Vector kjb::Abstract_dynamics::deltas

protected

The size of the step to take in the gradient direction. This step size is different for each parameter we are sampling over.

kjb::Vector kjb::Abstract_dynamics::gradients

protected

unsigned int kjb::Abstract_dynamics::kick

protected

kjb::Vector kjb::Abstract_dynamics::momenta

protected

kjb::MV_gaussian_distribution* kjb::Abstract_dynamics::mv_gauss

protected

kjb::Vector kjb::Abstract_dynamics::parameters

protected

The parameters to sample over.

double kjb::Abstract_dynamics::st_alpha

protected

kjb::Vector kjb::Abstract_dynamics::stochastic_momenta

protected

kjb::Vector kjb::Abstract_dynamics::stochastic_transition

protected

kjb::Vector kjb::Abstract_dynamics::temp_momenta

protected

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

• /work/kobus/src/lib/sample_cpp/abstract_dynamics.h
• /work/kobus/src/lib/sample_cpp/abstract_dynamics.cpp