IMP::core::MCCGSampler Class Reference

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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.

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).

Inheritance diagram for IMP::core::MCCGSampler:

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Public Member Functions
virtual std::string	get_type_name () const
virtual ::IMP::VersionInfo	get_version_info () const
	MCCGSampler (Model *m)
void	set_bounding_box (const algebra::BoundingBoxD< 3 > &bb)
	Set the bounding box for randomizing the Cartesian coordinates.
void	set_max_monte_carlo_step_size (FloatKey k, double d)
	Set the maximum size of the MC step for an attribute.
void	set_max_monte_carlo_step_size (double d)
	Set the maximum size of the MC step for all attributes.
void	set_number_of_attempts (unsigned int att)
	Set the maximum number of attempts to find a solution.
void	set_number_of_conjugate_gradient_steps (unsigned int cg)
	Set the number of CG steps to take after each MC step.
void	set_number_of_monte_carlo_steps (unsigned int cg)
	Set the number of MC steps to take in each optimization run.
Protected Member Functions
ConfigurationSet *	do_sample () const
Friends
template<class T >
void	IMP::internal::unref (T *)

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Member Function Documentation

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.

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

• MCCGSampler.h
• core/__init__.py