Detailed Description

A simple sampler.

This sampler randomizes the conformation and then uses Monte Carlo and conjugate gradient steps to search for good solutions. Each Monte Carlo move is followed by the specified number of conjugate gradient steps before it is decided whether to accept or reject the move. When the sampler encounters a solution that passes all of the restraint score cutoffs, it will accept the solution and move on to generating another one.

At the moment it only supports optimization of Cartesian coordinates, but this will be fixed when people ask for it (and they already have :-). We are also open to supporting a wider variety of optimization protocols (eg only do conjugate gradient steps occasionally).

Definition at line 40 of file MCCGSampler.h.

Public Member Functions
	MCCGSampler (kernel::Model *m, std::string name="MCCG Sampler %1%")

ConfigurationSet *	get_rejected_configurations () const

void	set_bounding_box (const algebra::BoundingBoxD< 3 > &bb)
	Set the bounding box for randomizing the Cartesian coordinates. More...

void	set_is_refining (bool tf)

void	set_local_optimizer (Optimizer *opt)
	Set a local optimizer to use instead of ConjugateGradients. More...

void	set_max_monte_carlo_step_size (double d)
	Set the maximum size of the MC step for all attributes. More...

void	set_max_monte_carlo_step_size (FloatKey k, double d)
	Set the maximum size of the MC step for an attribute. More...

void	set_save_rejected_configurations (bool tf)
	Whether or not to save rejected conformations. More...

Public Member Functions inherited from IMP::kernel::Sampler
	Sampler (kernel::Model *m, std::string name="Sampler %1%")

ConfigurationSet *	create_sample () const

Model *	get_model () const

ScoringFunction *	get_scoring_function () const

void	set_scoring_function (ScoringFunctionAdaptor sf)

Public Member Functions inherited from IMP::base::Object
virtual void	clear_caches ()

CheckLevel	get_check_level () const

LogLevel	get_log_level () const

void	set_check_level (CheckLevel l)

void	set_log_level (LogLevel l)
	Set the logging level used in this object. More...

void	set_was_used (bool tf) const

void	show (std::ostream &out=std::cout) const

const std::string &	get_name () const

void	set_name (std::string name)

Number of steps
A sampling run proceeds as 3 nested loops number of attempts number of Monte Carlo steps number of CG steps
void	set_number_of_attempts (unsigned int att)
	Set the maximum number of attempts to find a solution. More...

void	set_number_of_monte_carlo_steps (unsigned int cg)
	Set the number of MC steps to take in each optimization run. More...

void	set_number_of_conjugate_gradient_steps (unsigned int cg)
	Set the number of CG steps to take after each MC step. More...

Optimizer states
The optimizer states will be added to the MonteCarlo optimizer used.
void	remove_optimizer_state (OptimizerState *d)

void	remove_optimizer_states (const OptimizerStates &d)

void	set_optimizer_states (const OptimizerStates &ps)

void	set_optimizer_states_order (const OptimizerStates &objs)

unsigned int	add_optimizer_state (OptimizerState *obj)

void	add_optimizer_states (const OptimizerStates &objs)

void	clear_optimizer_states ()

unsigned int	get_number_of_optimizer_states () const

bool	get_has_optimizer_states ()

OptimizerState *	get_optimizer_state (unsigned int i) const

OptimizerStates	get_optimizer_states () const

void	reserve_optimizer_states (unsigned int sz)

virtual ConfigurationSet *	do_sample () const

virtual std::string	get_type_name () const

virtual ::IMP::base::VersionInfo	get_version_info () const
	Get information about the module and version of the object. More...

virtual	~MCCGSampler ()

Additional Inherited Members
Protected Member Functions inherited from IMP::base::Object
	Object (std::string name)
	Construct an object with the given name. More...

virtual void	do_destroy ()

Member Function Documentation

virtual ::IMP::base::VersionInfo IMP::core::MCCGSampler::get_version_info ( ) const

virtual

Get information about the module and version of the object.

Reimplemented from IMP::base::Object.

Definition at line 120 of file MCCGSampler.h.

void IMP::core::MCCGSampler::set_bounding_box ( const algebra::BoundingBoxD< 3 > & bb )

Set the bounding box for randomizing the Cartesian coordinates.

void IMP::core::MCCGSampler::set_is_refining ( bool tf )

if set to true, then do not randomize the configuration before sampling.

void IMP::core::MCCGSampler::set_local_optimizer ( Optimizer * opt )

Set a local optimizer to use instead of ConjugateGradients.

void IMP::core::MCCGSampler::set_max_monte_carlo_step_size ( double d )

Set the maximum size of the MC step for all attributes.

void IMP::core::MCCGSampler::set_max_monte_carlo_step_size	(	FloatKey	k,
		double	d
	)

Set the maximum size of the MC step for an attribute.

As was mentioned, at the moment k can be one of x,y or z.

void IMP::core::MCCGSampler::set_number_of_attempts ( unsigned int att )

Set the maximum number of attempts to find a solution.

void IMP::core::MCCGSampler::set_number_of_conjugate_gradient_steps ( unsigned int cg )

Set the number of CG steps to take after each MC step.

void IMP::core::MCCGSampler::set_number_of_monte_carlo_steps ( unsigned int cg )

Set the number of MC steps to take in each optimization run.

void IMP::core::MCCGSampler::set_save_rejected_configurations ( bool tf )

Whether or not to save rejected conformations.

Saving these can be useful if the sampling is not finding any good conformations.

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

MCCGSampler.h

Detailed Description

Public Member Functions

Number of steps

Optimizer states

Additional Inherited Members

Member Function Documentation