A Monte Carlo optimizer. More...

#include <IMP/core/MonteCarlo.h>

Inheritance diagram for IMP::core::MonteCarlo:

Public Member Functions
	MonteCarlo (kernel::Model *m)

	MonteCarlo ()

double	get_best_accepted_energy () const

double	get_last_accepted_energy () const

double	get_maximum_difference () 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.

void	set_maximum_difference (double d)

void	set_return_best (bool tf)

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

	Optimizer ()

void	clear_range_cache ()

Float	get_derivative (FloatIndex fi) const

double	get_last_score () const
	Return the score found in the last evaluate.

FloatIndexes	get_optimized_attributes () const

Restraints	get_restraints () const

double	get_scaled_derivative (FloatIndex fi) const

double	get_scaled_value (FloatIndex fi) const

double	get_score_threshold () const

ScoringFunction *	get_scoring_function () const
	Return the scoring function that is being used.

bool	get_stop_on_good_score () const

Float	get_value (FloatIndex fi) const

double	get_width (FloatKey k) const

double	optimize (unsigned int max_steps)
	Optimize the model for up to max_steps iterations. More...

void	set_restraints (const RestraintsTemp &rs)

void	set_scaled_value (FloatIndex fi, Float v) const

void	set_score_threshold (double s)

virtual void	set_scoring_function (ScoringFunctionAdaptor sf)

void	set_stop_on_good_score (bool tf)

void	set_value (FloatIndex fi, double v) const

double	width (FloatKey k) const

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)

Public Member Functions inherited from IMP::kernel::ModelObject
	ModelObject (kernel::Model *m, std::string name)

	ModelObject (std::string name)

bool	get_has_dependencies () const
	Return whether this object has dependencies computed.

bool	get_has_required_score_states () const

ModelObjectsTemp	get_inputs () const

ModelObjectsTemps	get_interactions () const

bool	get_is_part_of_model () const

Model *	get_model () const

ModelObjectsTemp	get_outputs () const

const ScoreStatesTemp &	get_required_score_states () const

void	set_has_dependencies (bool tf)

void	set_has_required_score_states (bool tf)

virtual void	set_model (kernel::Model *m)

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

virtual void	do_destroy ()

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)

Protected Member Functions
bool	do_accept_or_reject_move (double score, double last, double proposal_ratio)

bool	do_accept_or_reject_move (double score, double proposal_ratio)

virtual double	do_evaluate (const kernel::ParticleIndexes &moved) const
	Get the current energy. More...

MonteCarloMoverResult	do_move ()

virtual Float	do_optimize (unsigned int max_steps)
	override this function to do actual optimization

virtual void	do_step ()
	a class that inherits from this should override this method

kernel::ParticleIndexes	get_movable_particles () const

Protected Member Functions inherited from IMP::kernel::Optimizer
virtual ModelObjectsTemp	do_get_inputs () const

virtual ModelObjectsTemp	do_get_outputs () const
	don't return anything here to avoid pointless dependencies

ModelObjectsTemp	get_optimizer_state_inputs () const

void	update_states () const
	Update optimizer states, should be called at each successful step. More...

Protected Member Functions inherited from IMP::kernel::ModelObject
virtual ModelObjectsTemps	do_get_interactions () const

virtual void	handle_set_has_required_score_states (bool)

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

	Object ()

kT
The kT value has to be on the same scale as the differences in energy between good and bad states (and so the default is likely to not be a good choice).
void	set_kt (Float t)

Float	get_kt () const

Statistics
unsigned int	get_number_of_forward_steps () const
	Return how many times the optimizer has succeeded in taking a step.

unsigned int	get_number_of_upward_steps () const
	Return how many times the optimizer has stepped to higher energy.

unsigned int	get_number_of_proposed_steps () const
	Get number of proposed moves.

unsigned int	get_number_of_accepted_steps () const
	Get number of accepted moves.

void	reset_statistics ()

Movers
The following methods are used to manipulate the list of Movers. Each mover is called at each optimization step, giving it a chance to change the current configuration.
void	remove_mover (MonteCarloMover *d)

void	remove_movers (const MonteCarloMovers &d)

void	set_movers (const MonteCarloMovers &ps)

void	set_movers_order (const MonteCarloMovers &objs)

unsigned int	add_mover (MonteCarloMover *obj)

void	add_movers (const MonteCarloMovers &objs)

void	clear_movers ()

unsigned int	get_number_of_movers () const

bool	get_has_movers ()

MonteCarloMover *	get_mover (unsigned int i) const

MonteCarloMovers	get_movers () const

void	reserve_movers (unsigned int sz)

Incremental
Efficient evaluation of non-bonded list based restraints is a bit tricky with incremental evaluation.
void	set_incremental_scoring_function (IncrementalScoringFunction *isf)

bool	get_use_incremental_scoring_function () const

IncrementalScoringFunction *	get_incremental_scoring_function () const

Detailed Description

The optimizer uses a set of Mover objects to propose steps. At each sampling iteration, all Movers added to MonteCarlo are called to generate a new proposed configuration.

The movers propose some modification, which is then accepted or rejected based on the Metropolis criterion. Optionally, a number of local optimization steps are taken before the MonteCarlo step is accepted or rejected.

If you want to sequentially call one mover at every iteration, wrap all movers into a SerialMover first, and then add the SerialMover to MonteCarlo.

By default, the lowest score state encountered is returned.

See Also: Mover

Definition at line 47 of file MonteCarlo.h.

Constructor & Destructor Documentation

IMP::core::MonteCarlo::MonteCarlo ( )

Deprecated:: As of IMP release 2.1. See the IMP Deprecation Policy for more information about deprecation in IMP. Use the one that takes a model.

Member Function Documentation

bool IMP::core::MonteCarlo::do_accept_or_reject_move	(	double	score,
		double	last,
		double	proposal_ratio
	)

protected

Note that if return best is true, this will save the current state of the model. Also, if the move is accepted, the optimizer states will be updated.

virtual double IMP::core::MonteCarlo::do_evaluate ( const kernel::ParticleIndexes & moved ) const

protectedvirtual

By default it just calls Optimizer::get_scoring_function()->evaluate(false). However, if an incremental scoring function is used, the list of moved particles will be used to evaluate the score more efficiently. Also, if there is a maximum allowed difference in scores Optimizer::get_scoring_function()->evaluate_if_below() will be called instead, allowing more efficient evaluation. Classes which override this method should be similarly aware for efficiency.

The list of moved particles is passed.

Reimplemented in IMP::isd::HybridMonteCarlo.

Definition at line 180 of file MonteCarlo.h.

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double IMP::core::MonteCarlo::get_best_accepted_energy ( ) const

If return best is on, you can get the best energy found so far.

Definition at line 83 of file MonteCarlo.h.

double IMP::core::MonteCarlo::get_last_accepted_energy ( ) const

Return the energy of last accepted state.

Definition at line 79 of file MonteCarlo.h.

kernel::ParticleIndexes IMP::core::MonteCarlo::get_movable_particles ( ) const

protected

Get all movable particles (those that can be moved by the current movers.

void IMP::core::MonteCarlo::set_incremental_scoring_function ( IncrementalScoringFunction * isf )

Set whether to use incremental evaluate or evaluate all restraints each time. This cannot be changed during optimization.

void IMP::core::MonteCarlo::set_maximum_difference ( double d )

Computations can be acceletating by throwing out the tails of the distribution of accepted moves. To do this, specific a maximum acceptable difference between the before and after scores.

Definition at line 121 of file MonteCarlo.h.

void IMP::core::MonteCarlo::set_return_best ( bool tf )

By default, the optimizer returns the lowest score state found so far. If, instead, you wish to return the last accepted state, set return best to false.

Definition at line 63 of file MonteCarlo.h.

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

MonteCarlo.h

Public Member Functions

Protected Member Functions

kT

Statistics

Movers

Incremental

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation