#include <hmc_base.hpp>

Inheritance diagram for stan::mcmc::hmc_base< BaseRNG >:

Public Member Functions
	hmc_base (stan::model::prob_grad &model, double epsilon=-1, double epsilon_pm=0.0, bool epsilon_adapt=true, double delta=0.651, double gamma=0.05, BaseRNG rand_int=BaseRNG(std::time(0)), const std::vector< double > params_r=0, const std::vector< int > params_i=0)
	Construct a base HMC sampler. More...

virtual	~hmc_base ()

virtual void	set_params (const std::vector< double > &x, const std::vector< int > &z)
	Sets the model real and integer parameters to the specified values. More...

void	set_params_r (const std::vector< double > &x)
	Sets the model real parameters to the specified values and update gradients and log probability to match. More...

void	set_params_i (const std::vector< int > &z)
	Sets the model integer parameters to the specified values and update gradients and log probability to match. More...

bool	varying_epsilon ()

virtual void	adapt_off ()
	Turn off parameter adaptation. More...

virtual void	get_parameters (std::vector< double > &params)
	Write the step size into position 1 of the specified vector. More...

Public Member Functions inherited from stan::mcmc::adaptive_sampler
	adaptive_sampler (bool adapt, std::ostream error_msgs=0, std::ostream output_msgs=0)
	Constructs an adaptive sampler with specified adaptation status. More...

virtual	~adaptive_sampler ()
	Destructor. More...

void	set_error_stream (std::ostream &error_msgs)
	Set the stream into which errors will be written as the sampler runs. More...

void	unset_error_stream ()
	Unset the stream into which errors are written to 0 so that error messages are ignored. More...

void	set_output_stream (std::ostream &output_msgs)
	Set the stream into which output will be written as the sampler runs. More...

void	unset_output_stream ()
	Unset the stream into which errors are written to 0 so that output messages are ignored. More...

sample	next ()
	Returns the next sample from this sampler. More...

virtual sample	next_impl ()=0
	Returns the next sample from this sampler. More...

double	mean_stat ()
	Returns the value of the statistic we are trying to coerce. More...

void	set_mean_stat (double v)
	Sets the mean statistic to the specified value. More...

void	update_mean_stat (double avg_eta, double adapt_stat)
	Updates the mean statistic given the specified adaptation statistic and weighting. More...

unsigned int	nfevals ()
	Returns the number of times that the (possibly unnormalized) log probability function has been evaluated by this sampler. More...

void	nfevals_plus_eq (int n)
	Add the specified number of evaluations to the number of function evaluations. More...

int	n_steps ()
	Return the number of iterations for this sampler. More...

int	n_adapt_steps ()
	Return how many iterations parameter adaptation has happened for. More...

virtual void	adapt_on ()
	Turn on parameter adaptation. More...

bool	adapting ()
	Return whether or not parameter adaptation is on. More...

virtual void	write_sampler_params (std::ostream &o)
	Write out any sampler-specific parameters for output. More...

virtual void	write_adaptation_params (std::ostream &o)
	Use this method to write the adaptation parameters into the output. More...

virtual void	write_sampler_param_names (std::ostream &o)
	Write out any sampler-specific parameter names for output. More...

virtual void	get_sampler_param_names (std::vector< std::string > &names)
	Get any sampler-specific parameter namess. More...

virtual void	get_sampler_params (std::vector< double > &values)
	Get any sampler-specific parameters. More...

Protected Member Functions
virtual void	find_reasonable_parameters ()
	Search for a roughly reasonable (within a factor of 2) setting of the step size epsilon. More...

void	adaptation_init (double epsilon_scale)

Protected Attributes
stan::model::prob_grad &	_model

double	_epsilon

double	_epsilon_pm

double	_epsilon_last

bool	_epsilon_adapt

const double	_delta

const double	_gamma

DualAverage	_da

BaseRNG	_rand_int

boost::variate_generator< BaseRNG &, boost::normal_distribution<> >	_rand_unit_norm

boost::uniform_01< BaseRNG & >	_rand_uniform_01

std::vector< double >	_x

std::vector< int >	_z

std::vector< double >	_g

double	_logp

Protected Attributes inherited from stan::mcmc::adaptive_sampler
bool	_adapt

unsigned int	_n_steps

int	_n_adapt_steps

unsigned int	_nfevals

double	_mean_stat

std::ostream *	_error_msgs

std::ostream *	_output_msgs

Detailed Description

template<class BaseRNG = boost::mt19937>
class stan::mcmc::hmc_base< BaseRNG >

Definition at line 22 of file hmc_base.hpp.

Constructor & Destructor Documentation

◆ hmc_base()

template<class BaseRNG = boost::mt19937>

stan::mcmc::hmc_base< BaseRNG >::hmc_base	(	stan::model::prob_grad &	model,
		double	epsilon = `-1`,
		double	epsilon_pm = `0.0`,
		bool	epsilon_adapt = `true`,
		double	delta = `0.651`,
		double	gamma = `0.05`,
		BaseRNG	rand_int = `BaseRNG(std::time(0))`,
		const std::vector< double > *	params_r = `0`,
		const std::vector< int > *	params_i = `0`
	)

inline

Construct a base HMC sampler.

Parameters

model	Log probability model with gradients.
epsilon	Hamiltonian dynamics simulation step size. Optional; if not specified or set < 0, find_reasonable_parameters() will be called to initialize epsilon; default value = -1
epsilon_pm	Sample in range defined by plus-or minus this value over epsilon (sample step size uniformly in interval `[epsilon(1-epsilon_pm), epsilon(1+epsilon_pm)]`; default value = 0.0
epsilon_adapt	Flag indicating whether adaptation is turned on.
delta	Target acceptance rate for adaptation.
gamma	Tuning parameter for dual averaging adaptation.
rand_int	Base random integer generator.

Definition at line 115 of file hmc_base.hpp.

◆ ~hmc_base()

template<class BaseRNG = boost::mt19937>

virtual stan::mcmc::hmc_base< BaseRNG >::~hmc_base ( )

inlinevirtual

Definition at line 156 of file hmc_base.hpp.

Member Function Documentation

◆ adapt_off()

template<class BaseRNG = boost::mt19937>

virtual void stan::mcmc::hmc_base< BaseRNG >::adapt_off ( )

inlinevirtual

Turn off parameter adaptation.

Because we're using primal-dual averaging, once we're done adapting we want to set epsilon=the average value of epsilon over each adaptation step. This results in a lower-variance estimate of the optimal epsilon.

Reimplemented from stan::mcmc::adaptive_sampler.

Definition at line 228 of file hmc_base.hpp.

◆ adaptation_init()

template<class BaseRNG = boost::mt19937>

void stan::mcmc::hmc_base< BaseRNG >::adaptation_init ( double epsilon_scale )

inlineprotected

Definition at line 90 of file hmc_base.hpp.

◆ find_reasonable_parameters()

template<class BaseRNG = boost::mt19937>

virtual void stan::mcmc::hmc_base< BaseRNG >::find_reasonable_parameters ( )

inlineprotectedvirtual

Search for a roughly reasonable (within a factor of 2) setting of the step size epsilon.

Reimplemented from stan::mcmc::adaptive_sampler.

Definition at line 56 of file hmc_base.hpp.

◆ get_parameters()

template<class BaseRNG = boost::mt19937>

virtual void stan::mcmc::hmc_base< BaseRNG >::get_parameters ( std::vector< double > & params )

inlinevirtual

Write the step size into position 1 of the specified vector.

Parameters

[out] params Where to store epsilon.

Reimplemented from stan::mcmc::adaptive_sampler.

Definition at line 241 of file hmc_base.hpp.

◆ set_params()

template<class BaseRNG = boost::mt19937>

virtual void stan::mcmc::hmc_base< BaseRNG >::set_params	(	const std::vector< double > &	x,
		const std::vector< int > &	z
	)

inlinevirtual

Sets the model real and integer parameters to the specified values.

This method will typically be used to set the parameters by the client of this class after initialization.

Parameters

x	Real parameters.
z	Integer parameters.

Implements stan::mcmc::adaptive_sampler.

Definition at line 168 of file hmc_base.hpp.

◆ set_params_i()

template<class BaseRNG = boost::mt19937>

void stan::mcmc::hmc_base< BaseRNG >::set_params_i ( const std::vector< int > & z )

inline

Sets the model integer parameters to the specified values and update gradients and log probability to match.

This method will typically be used to set the parameters by the client of this class after initialization.

Parameters

z	Integer parameters.

Exceptions

std::invalid_argument if the number of integer parameters does not match the number of parameters defined by the model.

Definition at line 209 of file hmc_base.hpp.

◆ set_params_r()

template<class BaseRNG = boost::mt19937>

void stan::mcmc::hmc_base< BaseRNG >::set_params_r ( const std::vector< double > & x )

inline

Sets the model real parameters to the specified values and update gradients and log probability to match.

This method will typically be used to set the parrs by the client of this class after initialization.

Parameters

x	Real parameters.

Exceptions

std::invalid_argument if the number of real parameters does not match the number of parameters defined by the model.

Definition at line 190 of file hmc_base.hpp.

◆ varying_epsilon()

template<class BaseRNG = boost::mt19937>

bool stan::mcmc::hmc_base< BaseRNG >::varying_epsilon ( )

inline

Definition at line 216 of file hmc_base.hpp.

Member Data Documentation

◆ _da

template<class BaseRNG = boost::mt19937>

DualAverage stan::mcmc::hmc_base< BaseRNG >::_da

protected

Definition at line 36 of file hmc_base.hpp.

◆ _delta

template<class BaseRNG = boost::mt19937>

const double stan::mcmc::hmc_base< BaseRNG >::_delta

protected

Definition at line 34 of file hmc_base.hpp.

◆ _epsilon

template<class BaseRNG = boost::mt19937>

double stan::mcmc::hmc_base< BaseRNG >::_epsilon

protected

Definition at line 29 of file hmc_base.hpp.

◆ _epsilon_adapt

template<class BaseRNG = boost::mt19937>

bool stan::mcmc::hmc_base< BaseRNG >::_epsilon_adapt

protected

Definition at line 32 of file hmc_base.hpp.

◆ _epsilon_last

template<class BaseRNG = boost::mt19937>

double stan::mcmc::hmc_base< BaseRNG >::_epsilon_last

protected

Definition at line 31 of file hmc_base.hpp.

◆ _epsilon_pm

template<class BaseRNG = boost::mt19937>

double stan::mcmc::hmc_base< BaseRNG >::_epsilon_pm

protected

Definition at line 30 of file hmc_base.hpp.

◆ _g

template<class BaseRNG = boost::mt19937>

std::vector<double> stan::mcmc::hmc_base< BaseRNG >::_g

protected

Definition at line 49 of file hmc_base.hpp.

◆ _gamma

template<class BaseRNG = boost::mt19937>

const double stan::mcmc::hmc_base< BaseRNG >::_gamma

protected

Definition at line 35 of file hmc_base.hpp.

◆ _logp

template<class BaseRNG = boost::mt19937>

double stan::mcmc::hmc_base< BaseRNG >::_logp

protected

Definition at line 50 of file hmc_base.hpp.

◆ _model

template<class BaseRNG = boost::mt19937>

stan::model::prob_grad& stan::mcmc::hmc_base< BaseRNG >::_model

protected

Definition at line 27 of file hmc_base.hpp.

◆ _rand_int

template<class BaseRNG = boost::mt19937>

BaseRNG stan::mcmc::hmc_base< BaseRNG >::_rand_int

protected

Definition at line 38 of file hmc_base.hpp.

◆ _rand_uniform_01

template<class BaseRNG = boost::mt19937>

boost::uniform_01<BaseRNG&> stan::mcmc::hmc_base< BaseRNG >::_rand_uniform_01

protected

Definition at line 44 of file hmc_base.hpp.

◆ _rand_unit_norm

template<class BaseRNG = boost::mt19937>

boost::variate_generator<BaseRNG&, boost::normal_distribution<> > stan::mcmc::hmc_base< BaseRNG >::_rand_unit_norm

protected

Definition at line 41 of file hmc_base.hpp.

◆ _x

template<class BaseRNG = boost::mt19937>

std::vector<double> stan::mcmc::hmc_base< BaseRNG >::_x

protected

Definition at line 47 of file hmc_base.hpp.

◆ _z

template<class BaseRNG = boost::mt19937>

std::vector<int> stan::mcmc::hmc_base< BaseRNG >::_z

protected

Definition at line 48 of file hmc_base.hpp.

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

src/stan/mcmc/hmc_base.hpp

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

template<class BaseRNG = boost::mt19937> class stan::mcmc::hmc_base< BaseRNG >

Constructor & Destructor Documentation

◆ hmc_base()

◆ ~hmc_base()

Member Function Documentation

◆ adapt_off()

◆ adaptation_init()

◆ find_reasonable_parameters()

◆ get_parameters()

◆ set_params()

◆ set_params_i()

◆ set_params_r()

◆ varying_epsilon()

Member Data Documentation

◆ _da

◆ _delta

◆ _epsilon

◆ _epsilon_adapt

◆ _epsilon_last

◆ _epsilon_pm

◆ _g

◆ _gamma

◆ _logp

◆ _model

◆ _rand_int

◆ _rand_uniform_01

◆ _rand_unit_norm

◆ _x

◆ _z

template<class BaseRNG = boost::mt19937>
class stan::mcmc::hmc_base< BaseRNG >