FlatHistogram
-
class FlatHistogram : public feasst::Criteria
Flat histogram acceptance criteria uses a bias to improve sampling and attempt to recover the free energy of the system as a function of the give macrostate.
The Macrostate must be defined before the bias.
Public Functions
Construct with a macrostate and a bias.
Same as above, but with an added Constraint.
-
const Macrostate ¯ostate() const
Return the macrostate.
-
int num_iterations_to_complete() const
Return the number of iterations for a simulation to be complete. Iterations are defined by the Derived class. For example, one Metropolis iteration is 1000 trials. FlatHistogram iterations depend on the Bias. For TransitionMatrix, one iteration is a sweep.
-
void set_num_iterations_to_complete(const int num)
Set the number of iterations for a simulation to be complete.
-
int num_iterations(const int state = -1) const
Return the current number of iterations.
- param state:
If != -1, return iterations of a particular state (TM/WLTM only).
-
bool is_complete() const
Return true if the number of iterations for completion has been reached.
-
void set_complete()
Set the simulation as complete. Used for post processing.
-
bool is_accepted(const System &system, Acceptance *acceptance, Random *random)
Return whether or not the trial attempt should be accepted.
-
std::string write() const
Return a human-readable output of all data (not as brief as status).
-
int phase() const
Return the simulation phase index used to differentiate production and initialization, etc.
-
void increment_phase()
Increment the simulation phase.
-
int state() const
Return the state. Return -1 if state is not determined.
-
int num_states() const
Return the number of states. (default: 1).
-
void set_ln_prob(const LnProbability &ln_prob)
Set the macrostate probability distribution.
-
const LnProbability &ln_prob() const
Return the macrostate probability distribution.
-
void update()
Update.
Arguments
Bias: WangLandau, TransitionMatrix, WLTM, etc.
Criteria arguments.