IMP  2.0.1
The Integrative Modeling Platform
IMP::core::MonteCarlo Class Reference

A Monte Carlo optimizer. More...

#include <IMP/core/MonteCarlo.h>

+ Inheritance diagram for IMP::core::MonteCarlo:

Public Member Functions

 MonteCarlo (Model *m=nullptr)
 
double get_best_accepted_energy () const
 
double get_last_accepted_energy () const
 
double get_maximum_difference () const
 
void set_maximum_difference (double d)
 
void set_return_best (bool tf)
 
- Public Member Functions inherited from IMP::kernel::Optimizer
 Optimizer (Model *m, std::string name="Optimizer %1%")
 
double get_last_score () const
 Return the score found in the last evaluate.
 
Modelget_model () const
 Get the model being optimized.
 
ScoringFunctionget_scoring_function () const
 Return the scoring function that is being used.
 
bool get_stop_on_good_score () const
 
double optimize (unsigned int max_steps)
 Optimize the model for up to max_steps iterations. More...
 
void set_model (Model *m)
 Set the model being optimized. More...
 
virtual void set_scoring_function (ScoringFunctionAdaptor sf)
 
void set_stop_on_good_score (bool tf)
 
virtual void show (std::ostream &out=std::cout) const
 Print info about the optimizer state. More...
 
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 ()
 
OptimizerStateget_optimizer_state (unsigned int i) const
 
OptimizerStates get_optimizer_states () const
 
void reserve_optimizer_states (unsigned int sz)
 
- Public Member Functions inherited from IMP::base::Object
virtual void clear_caches ()
 
virtual IMP::base::VersionInfo get_version_info () const =0
 Get information about the module and version of the object.
 
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 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
 
ParticleIndexes get_movable_particles () const
 
- Protected Member Functions inherited from IMP::kernel::Optimizer
void update_states () const
 Update optimizer states, should be called at each successful step. More...
 
double width (FloatKey k) const
 
FloatIndexes get_optimized_attributes () const
 
void set_value (FloatIndex fi, double v) const
 
Float get_value (FloatIndex fi) const
 
Float get_derivative (FloatIndex fi) const
 
void set_scaled_value (FloatIndex fi, Float v) const
 
double get_scaled_value (FloatIndex fi) const
 
double get_scaled_derivative (FloatIndex fi) const
 
void clear_range_cache ()
 Clear the cache of range information. Do this at the start of optimization.
 
- Protected Member Functions inherited from IMP::base::Object
 Object (std::string name)
 Construct an object with the given name. More...
 

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.
 

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 ()
 
MonteCarloMoverget_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
 
IncrementalScoringFunctionget_incremental_scoring_function () const
 

Additional Inherited Members

Detailed Description

The optimizer uses a set of Mover objects to propose steps. Currently each Mover is called at each Monte Carlo iteration. If you only want to use one mover at a time, use a SerialMover. 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.

By default, the lowest score state encountered is returned.

See Also
Mover

Definition at line 42 of file MonteCarlo.h.

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 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 173 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 81 of file MonteCarlo.h.

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

Return the energy of last accepted state.

Definition at line 75 of file MonteCarlo.h.

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 104 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 55 of file MonteCarlo.h.


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