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IMP
2.1.1
The Integrative Modeling Platform
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IMP
2.1.1
The Integrative Modeling Platform
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A simple sampler. More...
#include <IMP/core/MCCGSampler.h>
Inheritance diagram for IMP::core::MCCGSampler: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. | |
| void | set_is_refining (bool tf) |
| void | set_local_optimizer (Optimizer *opt) |
| Set a local optimizer to use instead of ConjugateGradients. | |
| void | set_max_monte_carlo_step_size (double d) |
| Set the maximum size of the MC step for all attributes. | |
| 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 |
| ConfigurationSet * | get_sample () const |
| ScoringFunction * | get_scoring_function () const |
| void | set_scoring_function (ScoringFunctionAdaptor sf) |
| 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) |
Number of steps | |
A sampling run proceeds as 3 nested loops
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| void | set_number_of_attempts (unsigned int att) |
| Set the maximum number of attempts to find a solution. | |
| void | set_number_of_monte_carlo_steps (unsigned int cg) |
| Set the number of MC steps to take in each optimization run. | |
| void | set_number_of_conjugate_gradient_steps (unsigned int cg) |
| Set the number of CG steps to take after each MC step. | |
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. | |
Additional Inherited Members | |
| Protected Member Functions inherited from IMP::base::Object | |
| Object (std::string name) | |
| Construct an object with the given name. More... | |
| Object () | |
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 support 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.
| 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_max_monte_carlo_step_size | ( | FloatKey | k, |
| double | d | ||
| ) |
As was mentioned, at the moment k can be one of x,y or z.
| void IMP::core::MCCGSampler::set_save_rejected_configurations | ( | bool | tf | ) |
Saving these can be useful if the sampling is not finding any good conformations.