pub struct EntropicSamplingAdaptive<Hist, R, E, S, Res, T> { /* private fields */ }
Expand description
Entropic sampling made easy
J. Lee, “New Monte Carlo algorithm: Entropic sampling,” Phys. Rev. Lett. 71, 211–214 (1993), DOI: 10.1103/PhysRevLett.71.211
Implementations§
source§impl<Hist, R, E, S, Res, T> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
sourcepub fn energy(&self) -> &T
pub fn energy(&self) -> &T
Energy of ensemble
- assuming
energy_fn
(seeself.entropic_step
etc.) is deterministic and will allways give the same result for the same ensemble, this returns the energy of the current ensemble
sourcepub fn step_count(&self) -> usize
pub fn step_count(&self) -> usize
Number of entropic steps done until now
- will be reset by
self.refine_estimate
sourcepub fn step_goal(&self) -> usize
pub fn step_goal(&self) -> usize
Number of entropic steps to be performed
- if
self
was created fromWangLandauAdaptive
,step_goal
will be equal to the number of WangLandau steps, that were performed
sourcepub fn set_step_goal(&mut self, step_goal: usize)
pub fn set_step_goal(&mut self, step_goal: usize)
Number of entropic steps to be performed
- set the number of steps to be performed by entropic sampling
sourcepub fn min_step_size(&self) -> usize
pub fn min_step_size(&self) -> usize
sourcepub fn max_step_size(&self) -> usize
pub fn max_step_size(&self) -> usize
sourcepub fn best_of_steps(&self) -> &Vec<usize>
pub fn best_of_steps(&self) -> &Vec<usize>
Currently used best of
- might have length 0, if statistics are still being gathered
- otherwise this contains the step sizes, from which the next stepsize is drawn uniformly
sourcepub fn fraction_accepted_current(&self) -> f64
pub fn fraction_accepted_current(&self) -> f64
Fraction of steps accepted since the statistics were reset the last time
- (steps accepted since last reset) / (steps since last reset)
NaN
if no steps were performed yet
sourcepub fn total_entr_steps_accepted(&self) -> usize
pub fn total_entr_steps_accepted(&self) -> usize
sourcepub fn total_entr_steps_rejected(&self) -> usize
pub fn total_entr_steps_rejected(&self) -> usize
sourcepub fn fraction_accepted_total_entropic(&self) -> f64
pub fn fraction_accepted_total_entropic(&self) -> f64
Fraction of steps accepted since the creation of self
NaN
if no steps were performed yet
sourcepub fn log_density_estimate(&self) -> &Vec<f64>
pub fn log_density_estimate(&self) -> &Vec<f64>
- returns the (non normalized) log_density estimate log(P(E)), with which the simulation was started
- if you created this from a WangLandau simulation, this is the result of the WangLandau Simulation
sourcepub fn log_density_refined(&self) -> Vec<f64>where
Hist: Histogram,
pub fn log_density_refined(&self) -> Vec<f64>where
Hist: Histogram,
calculates the (non normalized) log_density estimate log(P(E)) according to the paper
source§impl<Hist, R, E, S, Res, T> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
sourcepub fn from_wl_adaptive(
wl: WangLandauAdaptive<Hist, R, E, S, Res, T>
) -> Result<Self, EntropicErrors>
pub fn from_wl_adaptive( wl: WangLandauAdaptive<Hist, R, E, S, Res, T> ) -> Result<Self, EntropicErrors>
Creates EntropicSamplingAdaptive from a WangLandauAdaptive
state
WangLandauAdaptive
state needs to be valid, i.e., you must have called one of theinit*
methods
- this ensures, that the members
old_energy
andold_bin
are notNone
sourcepub fn refine_estimate(&mut self) -> Vec<f64>
pub fn refine_estimate(&mut self) -> Vec<f64>
Calculates self.log_density_refined
and uses that as estimate for a the entropic sampling simulation
- returns old estimate
prepares self
for a new entropic simulation
- sets new estimate for log(P(E))
- resets statistic gathering
- resets step_count
sourcepub fn set_adjust_bestof_every(&mut self, adjust_bestof_every: usize)
pub fn set_adjust_bestof_every(&mut self, adjust_bestof_every: usize)
How often to adjust bestof_steps
?
- if you try to set a value smaller 10, it will be set to 10
- will reevalute the statistics every
adjust_bestof_every
steps,
- this will not start new statistics gathering but just trigger a reevaluation of the gathered statistics (should be O(max_stepsize - min_stepsize))
sourcepub fn is_rebuilding_statistics(&self) -> bool
pub fn is_rebuilding_statistics(&self) -> bool
Is the simulation in the process of rebuilding the statistics, i.e., is it currently trying many differnt step sizes?
sourcepub fn estimate_statistics(&self) -> Result<Vec<f64>, WangLandauErrors>
pub fn estimate_statistics(&self) -> Result<Vec<f64>, WangLandauErrors>
Estimate accept/reject statistics
- contains list of estimated probabilities for accepting a step of corresponding step size
- list[i] corresponds to step size
i + self.min_step
- O(trial_step_max - trial_step_min)
source§impl<Hist, R, E, S, Res, T> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
sourcepub unsafe fn entropic_sampling_while_unsafe<F, G, W>(
&mut self,
energy_fn: F,
print_fn: G,
condition: W
)
pub unsafe fn entropic_sampling_while_unsafe<F, G, W>( &mut self, energy_fn: F, print_fn: G, condition: W )
Entropic sampling
- performs
self.entropic_step(energy_fn)
untilcondition
is false - Note: you have access to the current step_count (
self.step_count()
)
Parameter
energy_fn
function calculatingSome(energy)
of the system or rather the Parameter of which you wish to obtain the probability distribution. If there are any states, for which the calculation is invalid,None
should be returned- steps resulting in ensembles for which
energy_fn(&mut ensemble)
isNone
will always be rejected - Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong! print_fn
: see below
Correlations
- if you want to measure correlations between “energy” and other measurable quantities,
use
print_fn
, which will be called after each step - use this function to write to a file or whatever you desire - Note: You do not have to recalculate the energy, if you need it in
print_fn
: just callself.energy()
- you have access to your ensemble with
self.ensemble()
- if you do not need it, you can use
|_|{}
asprint_fn
Safety
- While you do have mutable access to the ensemble, the energy function should not change the ensemble in a way, which affects the next calculation of the energy
- This is intended for usecases, where the energy calculation is more efficient with mutable access, e.g., through using a buffer stored in the ensemble
- Note: I chose to make this function unsafe to force users to aknowledge the (purely logical) limitations regarding the usage of the mutable ensemble. From a programming point of view this will not lead to any undefined behavior or such regardless of if the user fullfills the requirements
sourcepub fn entropic_sampling_while<F, G, W>(
&mut self,
energy_fn: F,
print_fn: G,
condition: W
)
pub fn entropic_sampling_while<F, G, W>( &mut self, energy_fn: F, print_fn: G, condition: W )
Entropic sampling
- performs
self.entropic_step(energy_fn)
untilcondition
is false - Note: you have access to the current step_count (
self.step_count()
)
Parameter
energy_fn
function calculatingSome(energy)
of the system or rather the Parameter of which you wish to obtain the probability distribution. If there are any states, for which the calculation is invalid,None
should be returned- steps resulting in ensembles for which
energy_fn(&mut ensemble)
isNone
will always be rejected - Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong! print_fn
: see below
Correlations
- if you want to measure correlations between “energy” and other measurable quantities,
use
print_fn
, which will be called after each step - use this function to write to a file or whatever you desire - Note: You do not have to recalculate the energy, if you need it in
print_fn
: just callself.energy()
- you have access to your ensemble with
self.ensemble()
- if you do not need it, you can use
|_|{}
asprint_fn
sourcepub fn entropic_sampling_while_acc<F, G, W>(
&mut self,
energy_fn: F,
print_fn: G,
condition: W
)
pub fn entropic_sampling_while_acc<F, G, W>( &mut self, energy_fn: F, print_fn: G, condition: W )
Entropic sampling using an accumulating markov step
- performs
self.entropic_step_acc(&mut energy_fn)
untilcondition(self) == false
Parameter
energy_fn
function calculating the energyE
of the system (or rather the Parameter of which you wish to obtain the probability distribution) during the markov steps, which can be more efficient.- Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong! print_fn
: see below
Correlations
- if you want to measure correlations between “energy” and other measurable quantities,
use
print_fn
, which will be called after each step - use this function to write to a file or whatever you desire - Note: You do not have to recalculate the energy, if you need it in
print_fn
: just callself.energy()
- you have access to your ensemble with
self.ensemble()
- if you do not need it, you can use
|_|{}
asprint_fn
sourcepub unsafe fn entropic_sampling_unsafe<F, G>(
&mut self,
energy_fn: F,
print_fn: G
)
pub unsafe fn entropic_sampling_unsafe<F, G>( &mut self, energy_fn: F, print_fn: G )
Entropic sampling
- if possible, use
self.entropic_sampling()
instead! - More powerful version of
self.entropic_sampling()
, since you now have mutable access - to access ensemble mutable, use
self.ensemble_mut()
- Note: Whatever you do with the ensemble (or self), should not change the result of the energy function, if performed again. Otherwise the results will be false!
- performs
self.entropic_step_unsafe(energy_fn)
untilself.step_count == self.step_goal
Parameter
energy_fn
function calculatingSome(energy)
of the system or rather the Parameter of which you wish to obtain the probability distribution. If there are any states, for which the calculation is invalid,None
should be returned- steps resulting in ensembles for which
energy_fn(&mut ensemble)
isNone
will always be rejected - Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong! print_fn
: see below
Correlations
- if you want to measure correlations between “energy” and other measurable quantities,
use
print_fn
, which will be called after each step - use this function to write to a file or whatever you desire - Note: You do not have to recalculate the energy, if you need it in
print_fn
: just callself.energy()
- you have access to your ensemble with
self.ensemble()
- if you do not need it, you can use
|_|{}
asprint_fn
Safety
- While you do have mutable access to the ensemble, the energy function should not change the ensemble in a way, which affects the next calculation of the energy
- This is intended for usecases, where the energy calculation is more efficient with mutable access, e.g., through using a buffer stored in the ensemble
- Note: I chose to make this function unsafe to force users to aknowledge the (purely logical) limitations regarding the usage of the mutable ensemble. From a programming point of view this will not lead to any undefined behavior or such regardless of if the user fullfills the requirements
sourcepub fn entropic_sampling<F, G>(&mut self, energy_fn: F, print_fn: G)
pub fn entropic_sampling<F, G>(&mut self, energy_fn: F, print_fn: G)
Entropic sampling
- performs
self.entropic_step(energy_fn)
untilself.step_count == self.step_goal
Parameter
energy_fn
function calculatingSome(energy)
of the system or rather the Parameter of which you wish to obtain the probability distribution. If there are any states, for which the calculation is invalid,None
should be returned- steps resulting in ensembles for which
energy_fn(&mut ensemble)
isNone
will always be rejected - Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong! print_fn
: see below
Correlations
- if you want to measure correlations between “energy” and other measurable quantities,
use
print_fn
, which will be called after each step - use this function to write to a file or whatever you desire - Note: You do not have to recalculate the energy, if you need it in
print_fn
: just callself.energy()
- you have access to your ensemble with
self.ensemble()
- if you do not need it, you can use
|_|{}
asprint_fn
sourcepub fn entropic_sampling_acc<F, G>(&mut self, energy_fn: F, print_fn: G)
pub fn entropic_sampling_acc<F, G>(&mut self, energy_fn: F, print_fn: G)
Entropic sampling using an accumulating markov step
- performs
self.entropic_step_acc(&mut energy_fn)
untilself.step_count == self.step_goal
Parameter
energy_fn
function calculating the energyE
of the system (or rather the Parameter of which you wish to obtain the probability distribution) during the markov steps, which can be more efficient.- Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong! print_fn
: see below
Correlations
- if you want to measure correlations between “energy” and other measurable quantities,
use
print_fn
, which will be called after each step - use this function to write to a file or whatever you desire - Note: You do not have to recalculate the energy, if you need it in
print_fn
: just callself.energy()
- you have access to your ensemble with
self.ensemble()
- if you do not need it, you can use
|_|{}
asprint_fn
sourcepub unsafe fn entropic_step_unsafe<F>(&mut self, energy_fn: F)
pub unsafe fn entropic_step_unsafe<F>(&mut self, energy_fn: F)
Entropic step
- performs a single step
Parameter
energy_fn
function calculatingSome(energy)
of the system or rather the Parameter of which you wish to obtain the probability distribution. If there are any states, for which the calculation is invalid,None
should be returned- steps resulting in ensembles for which
energy_fn(&mut ensemble)
isNone
will always be rejected
Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong!
Safety
- While you do have mutable access to the ensemble, the energy function should not change the ensemble in a way, which affects the next calculation of the energy
- This is intended for usecases, where the energy calculation is more efficient with mutable access, e.g., through using a buffer stored in the ensemble
- Note: I chose to make this function unsafe to force users to aknowledge the (purely logical) limitations regarding the usage of the mutable ensemble. From a programming point of view this will not lead to any undefined behavior or such regardless of if the user fullfills the requirements
sourcepub fn entropic_step<F>(&mut self, energy_fn: F)
pub fn entropic_step<F>(&mut self, energy_fn: F)
Entropic step
- performs a single step
Parameter
energy_fn
function calculatingSome(energy)
of the system or rather the Parameter of which you wish to obtain the probability distribution. If there are any states, for which the calculation is invalid,None
should be returned- steps resulting in ensembles for which
energy_fn(&mut ensemble)
isNone
will always be rejected
Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong!
sourcepub fn entropic_step_acc<F>(&mut self, energy_fn: F)
pub fn entropic_step_acc<F>(&mut self, energy_fn: F)
Accumulating entropic step
- performs a single step
Parameter
energy_fn
function calculating the energyE
of the system (or rather the Parameter of which you wish to obtain the probability distribution) during the markov steps, which can be more efficient.
Important
energy_fn
: should be the same as used for Wang Landau, otherwise the results will be wrong!
Trait Implementations§
source§impl<Hist: Clone, R: Clone, E: Clone, S: Clone, Res: Clone, T: Clone> Clone for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist: Clone, R: Clone, E: Clone, S: Clone, Res: Clone, T: Clone> Clone for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
source§fn clone(&self) -> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
fn clone(&self) -> EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
Returns a copy of the value. Read more
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from
source
. Read moresource§impl<Hist: Debug, R: Debug, E: Debug, S: Debug, Res: Debug, T: Debug> Debug for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist: Debug, R: Debug, E: Debug, S: Debug, Res: Debug, T: Debug> Debug for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
source§impl<'de, Hist, R, E, S, Res, T> Deserialize<'de> for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>where
Hist: Deserialize<'de>,
R: Deserialize<'de>,
E: Deserialize<'de>,
S: Deserialize<'de>,
T: Deserialize<'de>,
impl<'de, Hist, R, E, S, Res, T> Deserialize<'de> for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>where
Hist: Deserialize<'de>,
R: Deserialize<'de>,
E: Deserialize<'de>,
S: Deserialize<'de>,
T: Deserialize<'de>,
source§fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
source§impl<Hist, R, E, S, Res, T> Entropic for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> Entropic for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
source§fn step_counter(&self) -> usize
fn step_counter(&self) -> usize
Number of entropic steps done until now
- will be reset by
self.refine_estimate
source§fn step_goal(&self) -> usize
fn step_goal(&self) -> usize
Number of entropic steps to be performed
- if
self
was created fromWangLandauAdaptive
,step_goal
will be equal to the number of WangLandau steps, that were performed
source§fn total_steps_accepted(&self) -> usize
fn total_steps_accepted(&self) -> usize
How many steps were accepted until now? Read more
source§fn total_steps_rejected(&self) -> usize
fn total_steps_rejected(&self) -> usize
How many steps were rejected until now? Read more
source§fn write_log<W: Write>(&self, w: W) -> Result<(), Error>
fn write_log<W: Write>(&self, w: W) -> Result<(), Error>
Writes Information about the simulation to a file.
E.g. How many steps were performed.
source§fn steps_total(&self) -> usize
fn steps_total(&self) -> usize
Counter Read more
source§fn fraction_accepted_total(&self) -> f64
fn fraction_accepted_total(&self) -> f64
Calculate, which fraction of steps were accepted Read more
source§fn fraction_rejected_total(&self) -> f64
fn fraction_rejected_total(&self) -> f64
Calculate, which fraction of steps were rejected Read more
source§fn is_finished(&self) -> bool
fn is_finished(&self) -> bool
Checks wang landau threshold Read more
source§impl<Hist, R, E, S, Res, Energy> EntropicEnergy<Energy> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
impl<Hist, R, E, S, Res, Energy> EntropicEnergy<Energy> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
source§fn energy(&self) -> &Energy
fn energy(&self) -> &Energy
Energy of ensemble
- assuming
energy_fn
(seeself.entropic_step
etc.) is deterministic and will allways give the same result for the same ensemble, this returns the energy of the current ensemble
source§impl<Hist, R, E, S, Res, Energy> EntropicEnsemble<E> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
impl<Hist, R, E, S, Res, Energy> EntropicEnsemble<E> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
source§impl<Hist, R, E, S, Res, Energy> EntropicHist<Hist> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
impl<Hist, R, E, S, Res, Energy> EntropicHist<Hist> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
source§impl<Hist, R, E, S, Res, Energy> GlueAble<Hist> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
impl<Hist, R, E, S, Res, Energy> GlueAble<Hist> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>
fn push_glue_entry_ignoring( &self, job: &mut GlueJob<Hist>, ignore_idx: &[usize] )
fn push_glue_entry(&self, job: &mut GlueJob<H>)
source§impl<Hist, R, E, S, Res, Energy> HasRng<R> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>where
R: Rng,
impl<Hist, R, E, S, Res, Energy> HasRng<R> for EntropicSamplingAdaptive<Hist, R, E, S, Res, Energy>where
R: Rng,
source§impl<Hist, R, E, S, Res, T> Serialize for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> Serialize for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
source§impl<Hist, R, E, S, Res, T> TryFrom<WangLandauAdaptive<Hist, R, E, S, Res, T>> for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> TryFrom<WangLandauAdaptive<Hist, R, E, S, Res, T>> for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
§type Error = EntropicErrors
type Error = EntropicErrors
The type returned in the event of a conversion error.
Auto Trait Implementations§
impl<Hist, R, E, S, Res, T> RefUnwindSafe for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>where
E: RefUnwindSafe,
Hist: RefUnwindSafe,
R: RefUnwindSafe,
Res: RefUnwindSafe,
S: RefUnwindSafe,
T: RefUnwindSafe,
impl<Hist, R, E, S, Res, T> Send for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> Sync for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> Unpin for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>
impl<Hist, R, E, S, Res, T> UnwindSafe for EntropicSamplingAdaptive<Hist, R, E, S, Res, T>where
E: UnwindSafe,
Hist: UnwindSafe,
R: UnwindSafe,
Res: UnwindSafe,
S: UnwindSafe,
T: UnwindSafe,
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
§impl<S, T> Cast<T> for Swhere
T: Conv<S>,
impl<S, T> Cast<T> for Swhere
T: Conv<S>,
§impl<S, T> CastApprox<T> for Swhere
T: ConvApprox<S>,
impl<S, T> CastApprox<T> for Swhere
T: ConvApprox<S>,
§fn try_cast_approx(self) -> Result<T, Error>
fn try_cast_approx(self) -> Result<T, Error>
§fn cast_approx(self) -> T
fn cast_approx(self) -> T
§impl<S, T> CastFloat<T> for Swhere
T: ConvFloat<S>,
impl<S, T> CastFloat<T> for Swhere
T: ConvFloat<S>,
§fn cast_trunc(self) -> T
fn cast_trunc(self) -> T
Cast to integer, truncating Read more
§fn cast_nearest(self) -> T
fn cast_nearest(self) -> T
Cast to the nearest integer Read more
§fn cast_floor(self) -> T
fn cast_floor(self) -> T
Cast the floor to an integer Read more
§fn try_cast_trunc(self) -> Result<T, Error>
fn try_cast_trunc(self) -> Result<T, Error>
Try converting to integer with truncation Read more
§fn try_cast_nearest(self) -> Result<T, Error>
fn try_cast_nearest(self) -> Result<T, Error>
Try converting to the nearest integer Read more
§fn try_cast_floor(self) -> Result<T, Error>
fn try_cast_floor(self) -> Result<T, Error>
Try converting the floor to an integer Read more
§fn try_cast_ceil(self) -> Result<T, Error>
fn try_cast_ceil(self) -> Result<T, Error>
Try convert the ceiling to an integer Read more