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use{
crate::{
*,
rees::replica_exchange,
rewl::{Rewl, ignore_fn},
glue_helper::{subtract_max, ln_to_log10},
glue::derivative::*
},
rand::{Rng, prelude::SliceRandom},
std::{num::NonZeroUsize, sync::*, cmp::*},
rayon::prelude::*
};
#[cfg(feature = "sweep_time_optimization")]
use std::cmp::Reverse;
#[cfg(feature = "serde_support")]
use serde::{Serialize, Deserialize};
/// # Struct used for entropic sampling with replica exchanges
/// See [this](crate::rees), also for merge functions to create the
/// final probability density functions
#[derive(Debug)]
#[cfg_attr(feature = "serde_support", derive(Serialize, Deserialize))]
pub struct ReplicaExchangeEntropicSampling<Extra, Ensemble, R, Hist, Energy, S, Res>
{
pub(crate) chunk_size: NonZeroUsize,
pub(crate) ensembles: Vec<RwLock<Ensemble>>,
pub(crate) walker: Vec<ReesWalker<R, Hist, Energy, S, Res>>,
pub(crate) replica_exchange_mode: bool,
pub(crate) extra: Vec<Extra>,
pub(crate) rewl_roundtrips: Vec<usize>,
pub(crate) rees_roundtrip_halfes: Vec<usize>,
pub(crate) rees_last_extreme_interval_visited: Vec<ExtremeInterval>
}
impl<Extra, Ensemble, R, Hist, Energy, S, Res> GlueAble<Hist> for ReplicaExchangeEntropicSampling<Extra, Ensemble, R, Hist, Energy, S, Res>
where Hist: Clone + Histogram
{
fn push_glue_entry_ignoring(
&self,
job: &mut GlueJob<Hist>,
ignore_idx: &[usize]
) {
job.round_trips
.extend(self.rees_roundtrip_iter());
let (hists, probs) = self.get_log_prob_and_hists();
self.walker
.chunks(self.chunk_size.get())
.zip(hists)
.zip(probs)
.enumerate()
.filter_map(|(index, ((walker, hist), prob))|
{
if ignore_idx.contains(&index){
None
} else {
Some(((walker, hist), prob))
}
}
)
.for_each(
|((walker, hist), prob)|
{
let mut missing_steps = 0;
let mut accepted = 0;
let mut rejected = 0;
let mut replica_exchanges = 0;
let mut proposed_replica_exchanges = 0;
for w in walker{
if !w.is_finished(){
let missing = w.step_threshold() - w.step_count();
if missing > missing_steps{
missing_steps = missing;
}
}
let r = w.rejected_markov_steps();
let a = w.step_count() - r;
rejected += r;
accepted += a;
replica_exchanges += w.replica_exchanges();
proposed_replica_exchanges += w.proposed_replica_exchanges();
}
let stats = IntervalSimStats{
sim_progress: SimProgress::MissingSteps(missing_steps),
interval_sim_type: SimulationType::REES,
rejected_steps: rejected,
accepted_steps: accepted,
replica_exchanges: Some(replica_exchanges),
proposed_replica_exchanges: Some(proposed_replica_exchanges),
merged_over_walkers: self.chunk_size
};
job.collection.push(
GlueEntry{
hist: hist.clone(),
prob,
log_base: LogBase::BaseE,
interval_stats: stats
}
);
}
)
}
}
/// # Short for [ReplicaExchangeEntropicSampling]
pub type Rees<Extra, Ensemble, R, Hist, Energy, S, Res> = ReplicaExchangeEntropicSampling<Extra, Ensemble, R, Hist, Energy, S, Res>;
impl<Ensemble, R, Hist, Energy, S, Res, Extra> Rees<Extra, Ensemble, R, Hist, Energy, S, Res>
{
/// # Iterator over ensembles
/// If you do not know what `RwLockReadGuard<'a, Ensemble>` is - do not worry.
/// you can just pretend it is `&Ensemble` and everything should work out fine,
/// since it implements [`Deref`](https://doc.rust-lang.org/std/ops/trait.Deref.html).
/// Of cause, you can also take a look at [`RwLockReadGuard`](https://doc.rust-lang.org/std/sync/struct.RwLockReadGuard.html)
pub fn ensemble_iter(&'_ self) -> impl Iterator<Item=RwLockReadGuard<'_, Ensemble>>
{
self.ensembles
.iter()
.map(|e| e.read().unwrap())
}
/// # read access to your ensembles
/// * None if index out of range
/// * If you do not know what `RwLockReadGuard<Ensemble>` is - do not worry.
/// you can just pretend it is `&Ensemble` and everything will work out fine,
/// since it implements [`Deref`](https://doc.rust-lang.org/std/ops/trait.Deref.html).
/// Of cause, you can also take a look at [`RwLockReadGuard`](https://doc.rust-lang.org/std/sync/struct.RwLockReadGuard.html)
pub fn get_ensemble(&self, index: usize) -> Option<RwLockReadGuard<Ensemble>>
{
self.ensembles
.get(index)
.map(|e| e.read().unwrap())
}
/// # mut access to your ensembles
/// * if possible, prefer [`get_ensemble`](Self::get_ensemble)
/// * **unsafe** only use this if you know what you are doing
/// * `None` if `index` out of range
/// ## Safety
/// * might **panic** if a thread is poisened
/// * it is assumed, that whatever you change has no effect on the
/// Markov Chain, the result of the energy function etc.
pub unsafe fn get_ensemble_mut(&mut self, index: usize) -> Option<&mut Ensemble>
{
self.ensembles
.get_mut(index)
.map(|e| e.get_mut().unwrap())
}
/// # Mutable iterator over ensembles
/// * if possible, prefer [`ensemble_iter`](Self::ensemble_iter)
/// ## Safety
/// * it is assumed, that whatever you change has no effect on the
/// Markov Chain, the result of the energy function etc.
/// * might **panic** if a thread is poisened
pub unsafe fn ensemble_iter_mut(&mut self) -> impl Iterator<Item=&mut Ensemble>
{
self.ensembles
.iter_mut()
.map(|item| item.get_mut().unwrap())
}
/// # read access to the internal histograms used by the walkers
pub fn hists(&self) -> Vec<&Hist>
{
self.walker.iter()
.map(|w| w.hist())
.collect()
}
/// # read access to internal histogram
/// * None if index out of range
pub fn get_hist(&self, index: usize) -> Option<&Hist>
{
self.walker
.get(index)
.map(|w| w.hist())
}
fn get_log_prob_and_hists(&self) -> (Vec<&Hist>, Vec<Vec<f64>>)
where Hist: Histogram
{
// get the log_probabilities - the walkers over the same intervals are merged
let log_prob: Vec<_> = self.walker
.chunks(self.chunk_size.get())
.map(get_merged_walker_prob)
.collect();
let hists: Vec<_> = self.walker.iter()
.step_by(self.chunk_size.get())
.map(|w| w.hist())
.collect();
(hists, log_prob)
}
/// # Iterate over the roundtrips done by the REWL
/// This returns an Iterator which returns the number of roundtrips for each walker.
/// Roundtrips are defined as follows:
///
/// If a walker is in the leftest interval, then in the rightest and then in the leftest again
/// (or the other way around) then this is counted as one roundtrip.
/// Note: If only one interval exists, no roundtrips are possible
///
/// This iterator will return the roundtrips from the REWL simulation
pub fn rewl_roundtrip_iter(&'_ self) -> impl Iterator<Item=usize> + '_
{
self.rewl_roundtrips
.iter()
.copied()
}
/// # Iterator over roundtrips done by REES
/// - same as [rewl_roundtrip_iter](Self::rewl_roundtrip_iter) just for the rees roundtrips
pub fn rees_roundtrip_iter(&'_ self) -> impl Iterator<Item=usize> + '_
{
self.rees_roundtrip_halfes
.iter()
.map(|half| half / 2)
}
}
impl<Ensemble, R, Hist, Energy, S, Res> From<Rewl<Ensemble, R, Hist, Energy, S, Res>> for Rees<(), Ensemble, R, Hist, Energy, S, Res>
where Hist: Histogram
{
fn from(rewl: Rewl<Ensemble, R, Hist, Energy, S, Res>) -> Self
{
let extra = vec![(); rewl.walker.len()];
let rees_result = rewl.into_rees_with_extra(extra);
match rees_result{
Ok(rees) => rees,
Err(_) => unreachable!()
}
}
}
impl<Extra, Ensemble, R, Hist, Energy, S, Res> Rees<Extra, Ensemble, R, Hist, Energy, S, Res>
{
/// # Checks threshold
/// returns true, if all walkers are [finished](`crate::rees::ReesWalker::is_finished`)
pub fn is_finished(&self) -> bool
{
self.walker
.iter()
.all(|w| w.is_finished())
}
/// # Get the number of intervals present
pub fn num_intervals(&self) -> usize
{
self.walker.len() / self.chunk_size.get()
}
/// # How many walkers are there in total?
pub fn num_walkers(&self) -> usize
{
self.walker.len()
}
/// Returns number of walkers per interval
pub fn walkers_per_interval(&self) -> NonZeroUsize
{
self.chunk_size
}
/// # Returns internal walkers
/// * access to internal slice of walkers
/// * the walkers are sorted and neighboring walker are either
/// sampling the same interval, or a neighboring
/// (and if the replica exchange makes any sense overlapping)
/// interval
pub fn walkers(&self) -> &[ReesWalker<R, Hist, Energy, S, Res>]
{
&self.walker
}
/// # Change step size for markov chain of walkers
/// * changes the step size used in the sweep
/// * changes step size of all walkers in the nth interval
/// * returns Err if index out of bounds, i.e., the requested interval does not exist
/// * interval counting starts at 0, i.e., n=0 is the first interval
#[allow(clippy::result_unit_err)]
pub fn change_step_size_of_interval(&mut self, n: usize, step_size: usize) -> Result<(), ()>
{
let start = n * self.chunk_size.get();
let end = start + self.chunk_size.get();
if self.walker.len() < end {
Err(())
} else {
let slice = &mut self.walker[start..start+self.chunk_size.get()];
slice.iter_mut()
.for_each(|entry| entry.step_size_change(step_size));
Ok(())
}
}
/// # Get step size for markov chain of walkers
/// * returns `None` if index out of bounds, i.e., the requested interval does not exist
/// * interval counting starts at 0, i.e., n=0 is the first interval
pub fn get_step_size_of_interval(&self, n: usize) -> Option<usize>
{
let start = n * self.chunk_size.get();
let end = start + self.chunk_size.get();
if self.walker.len() < end {
None
} else {
let slice = &self.walker[start..start+self.chunk_size.get()];
let step_size = slice[0].step_size();
slice[1..]
.iter()
.for_each(|w|
assert_eq!(
step_size, w.step_size(),
"Fatal Error encountered; ERRORCODE 0x8 - \
Sweep sizes of intervals do not match! \
This should be impossible! if you are using the latest version of the \
'sampling' library, please contact the library author via github by opening an \
issue! https://github.com/Pardoxa/sampling/issues"
)
);
Some(step_size)
}
}
/// # Change sweep size for markov chain of walkers
/// * changes the sweep size used in the sweep
/// * changes sweep size of all walkers in the nth interval
/// * returns Err if index out of bounds, i.e., the requested interval does not exist
/// * interval counting starts at 0, i.e., n=0 is the first interval
#[allow(clippy::result_unit_err)]
pub fn change_sweep_size_of_interval(&mut self, n: usize, sweep_size: NonZeroUsize) -> Result<(), ()>
{
let start = n * self.chunk_size.get();
let end = start + self.chunk_size.get();
if self.walker.len() < end {
Err(())
} else {
let slice = &mut self.walker[start..start+self.chunk_size.get()];
slice.iter_mut()
.for_each(|entry| entry.sweep_size_change(sweep_size));
Ok(())
}
}
/// # Get sweep size for markov chain of walkers
/// * returns `None` if index out of bounds, i.e., the requested interval does not exist
/// * interval counting starts at 0, i.e., n=0 is the first interval
pub fn get_sweep_size_of_interval(&self, n: usize) -> Option<NonZeroUsize>
{
let start = n * self.chunk_size.get();
let end = start + self.chunk_size.get();
if self.walker.len() < end {
None
} else {
let slice = &self.walker[start..start+self.chunk_size.get()];
let sweep_size = slice[0].sweep_size();
slice[1..]
.iter()
.for_each(|w|
assert_eq!(
sweep_size, w.sweep_size(),
"Fatal Error encountered; ERRORCODE 0x2 - \
Sweep sizes of intervals do not match! \
This should be impossible! if you are using the latest version of the \
'sampling' library, please contact the library author via github by opening an \
issue! https://github.com/Pardoxa/sampling/issues"
)
);
Some(sweep_size)
}
}
/// # Read access to your extra information
pub fn extra_slice(&self) -> &[Extra]
{
&self.extra
}
/// # Write access to your extra information
pub fn extra_slice_mut(&mut self) -> &mut[Extra]
{
&mut self.extra
}
/// # Remove extra vector
/// * returns tuple of Self (without extra, i.e., `Rees<(), Ensemble, R, Hist, Energy, S, Res>`)
/// and vector of Extra
#[allow(clippy::type_complexity)]
pub fn unpack_extra(self) -> (Rees<(), Ensemble, R, Hist, Energy, S, Res>, Vec<Extra>)
{
let old_extra = self.extra;
let extra = vec![(); old_extra.len()];
let rees = Rees{
extra,
walker: self.walker,
chunk_size: self.chunk_size,
ensembles: self.ensembles,
replica_exchange_mode: self.replica_exchange_mode,
rees_last_extreme_interval_visited: self.rees_last_extreme_interval_visited,
rees_roundtrip_halfes: self.rees_roundtrip_halfes,
rewl_roundtrips: self.rewl_roundtrips
};
(
rees,
old_extra
)
}
/// # Swap the extra vector
/// * Note: len of extra has to be the same as `self.num_walkers()` (which is the same as `self.extra_slice().len()`)
/// otherwise an Err is returned
#[allow(clippy::result_unit_err, clippy::type_complexity)]
pub fn swap_extra<Extra2>(
self,
new_extra: Vec<Extra2>
) -> Result<(Rees<Extra2, Ensemble, R, Hist, Energy, S, Res>, Vec<Extra>), ()>
{
if self.extra.len() != new_extra.len(){
Err(())
} else {
let old_extra = self.extra;
let rees = Rees{
extra: new_extra,
walker: self.walker,
chunk_size: self.chunk_size,
ensembles: self.ensembles,
replica_exchange_mode: self.replica_exchange_mode,
rees_last_extreme_interval_visited: self.rees_last_extreme_interval_visited,
rees_roundtrip_halfes: self.rees_roundtrip_halfes,
rewl_roundtrips: self.rewl_roundtrips
};
Ok(
(
rees,
old_extra
)
)
}
}
pub(crate) fn update_roundtrips(&mut self)
{
if self.num_intervals() <= 1 {
return;
}
// check all walker that are currently in the first interval
let mut chunk_iter = self.walker.chunks(self.chunk_size.get());
let first_chunk = chunk_iter.next().unwrap();
first_chunk.iter()
.for_each(
|walker|
{
let id = walker.id();
let last_visited = match self.rees_last_extreme_interval_visited.get_mut(id){
Some(last) => last,
None => unreachable!()
};
match last_visited {
ExtremeInterval::Right => {
*last_visited = ExtremeInterval::Left;
self.rees_roundtrip_halfes[id] += 1;
},
ExtremeInterval::None => {
*last_visited = ExtremeInterval::Left;
},
_ => ()
}
}
);
// check all walker that are currently in the last interval
let last_chunk = match chunk_iter.last()
{
Some(chunk) => chunk,
None => unreachable!()
};
last_chunk.iter()
.for_each(
|walker|
{
let id = walker.id();
let last_visited = match self.rees_last_extreme_interval_visited.get_mut(id){
Some(last) => last,
None => unreachable!()
};
match last_visited {
ExtremeInterval::Left => {
*last_visited = ExtremeInterval::Right;
self.rees_roundtrip_halfes[id] += 1;
},
ExtremeInterval::None => {
*last_visited = ExtremeInterval::Right;
},
_ => ()
}
}
);
}
}
impl<Ensemble, R, Hist, Energy, S, Res> Rees<(), Ensemble, R, Hist, Energy, S, Res>
{
/// # Add extra information to your Replica Exchange entropic sampling simulation
/// * can be used to, e.g., print stuff during the simulation, or write it to a file and so on
#[allow(clippy::type_complexity, clippy::result_large_err)]
pub fn add_extra<Extra>(
self,
extra: Vec<Extra>
) -> Result<Rees<Extra, Ensemble, R, Hist, Energy, S, Res>, (Self, Vec<Extra>)>
{
if self.walker.len() != extra.len(){
Err(
(
self,
extra
)
)
} else {
let rees = Rees{
extra,
walker: self.walker,
chunk_size: self.chunk_size,
ensembles: self.ensembles,
replica_exchange_mode: self.replica_exchange_mode,
rees_last_extreme_interval_visited: self.rees_last_extreme_interval_visited,
rees_roundtrip_halfes: self.rees_roundtrip_halfes,
rewl_roundtrips: self.rewl_roundtrips
};
Ok(
rees
)
}
}
}
impl<Extra, Ensemble, R, Hist, Energy, S, Res> Rees<Extra, Ensemble, R, Hist, Energy, S, Res>
where Ensemble: Send + Sync + MarkovChain<S, Res>,
R: Send + Sync + Rng,
Extra: Send + Sync,
Hist: Send + Sync + Histogram + HistogramVal<Energy>,
Energy: Send + Sync + Clone,
S: Send + Sync,
Res: Send + Sync,
{
/// # Refine the estimate of the probability density functions
/// * refines the estimate of all walkers
/// * does so by calling the walker method [refine](`crate::rees::ReesWalker::refine`)
pub fn refine(&mut self)
{
self.walker
.par_iter_mut()
.for_each(|w| w.refine());
}
/// # Sweep
/// * Performs one sweep of the Replica exchange entropic sampling simulation
/// * You can make a complete simulation, by repeatatly calling this method
/// until `self.is_finished()` returns true
pub fn sweep<F, P>
(
&mut self,
energy_fn: F,
extra_fn: P
)
where F: Fn(&mut Ensemble) -> Option<Energy> + Copy + Send + Sync,
P: Fn(&ReesWalker<R, Hist, Energy, S, Res>, &mut Ensemble, &mut Extra) + Copy + Send + Sync,
{
let slice = self.ensembles.as_slice();
#[cfg(not(feature = "sweep_time_optimization"))]
let walker = &mut self.walker;
#[cfg(feature = "sweep_time_optimization")]
let mut walker =
{
let mut walker = Vec::with_capacity(self.walker.len());
walker.extend(
self.walker.iter_mut()
);
walker.par_sort_unstable_by_key(|w| Reverse(w.duration()));
walker
};
walker
.par_iter_mut()
.zip(self.extra.par_iter_mut())
.for_each(|(w, extra)| w.sweep(slice, extra, extra_fn, energy_fn));
// replica exchange
if self.walkers_per_interval().get() > 1 {
let exchange_m = self.replica_exchange_mode;
self.walker
.par_chunks_mut(self.chunk_size.get())
.for_each(
|chunk|
{
let mut shuf = Vec::with_capacity(chunk.len());
if let Some((first, rest)) = chunk.split_first_mut(){
shuf.extend(
rest.iter_mut()
);
shuf.shuffle(&mut first.rng);
shuf.push(first);
let s = if exchange_m {
&mut shuf
} else {
&mut shuf[1..]
};
s.chunks_exact_mut(2)
.for_each(
|c|
{
let ptr = c.as_mut_ptr();
unsafe{
let a = &mut *ptr;
let b = &mut *ptr.offset(1);
replica_exchange(a, b);
}
}
);
}
}
);
}
let walker_slice = if self.replica_exchange_mode
{
&mut self.walker
} else {
&mut self.walker[self.chunk_size.get()..]
};
self.replica_exchange_mode = !self.replica_exchange_mode;
let chunk_size = self.chunk_size;
walker_slice
.par_chunks_exact_mut(2 * self.chunk_size.get())
.for_each(
|walker_chunk|
{
let (slice_a, slice_b) = walker_chunk.split_at_mut(chunk_size.get());
let mut slice_b_shuffled: Vec<_> = slice_b.iter_mut().collect();
slice_b_shuffled.shuffle(&mut slice_a[0].rng);
for (walker_a, walker_b) in slice_a.iter_mut()
.zip(slice_b_shuffled.into_iter())
{
replica_exchange(walker_a, walker_b);
}
}
);
self.update_roundtrips()
}
/// # Perform the Replica exchange simulation
/// * will simulate until **all** walkers are finished
/// * extra_fn should be used for example for writing Data to a file
pub fn simulate_until_convergence<F, P>(
&mut self,
energy_fn: F,
extra_fn: P
)
where
Ensemble: Send + Sync,
R: Send + Sync,
F: Fn(&mut Ensemble) -> Option<Energy> + Copy + Send + Sync,
P: Fn(&ReesWalker<R, Hist, Energy, S, Res>, &mut Ensemble, &mut Extra) + Copy + Send + Sync,
{
while !self.is_finished()
{
self.sweep(energy_fn, extra_fn);
}
}
/// # Perform the Replica exchange simulation
/// * will simulate until **all** walkers are finished **or**
/// * until condition returns false
pub fn simulate_while<F, C, P>(
&mut self,
energy_fn: F,
mut condition: C,
extra_fn: P
)
where
Ensemble: Send + Sync,
R: Send + Sync,
F: Fn(&mut Ensemble) -> Option<Energy> + Copy + Send + Sync,
C: FnMut(&Self) -> bool,
P: Fn(&ReesWalker<R, Hist, Energy, S, Res>, &mut Ensemble, &mut Extra) + Copy + Send + Sync,
{
while !self.is_finished() && condition(self)
{
self.sweep(energy_fn, extra_fn);
}
}
/// # Sanity check
/// * checks if the stored (i.e., last) energy(s) of the system
/// match with the result of energy_fn
pub fn check_energy_fn<F>(
&mut self,
energy_fn: F
) -> bool
where Energy: PartialEq,
F: Fn(&mut Ensemble) -> Option<Energy> + Copy + Send + Sync,
{
let ensembles = self.ensembles.as_slice();
self.walker
.par_iter()
.all(|w| w.check_energy_fn(ensembles, energy_fn))
}
#[allow(clippy::type_complexity)]
#[deprecated]
fn merged_log_probability_helper(&self) -> Result<(Vec<usize>, Vec<usize>, Vec<Vec<f64>>, Hist), HistErrors>
where Hist: HistogramCombine
{
// get the log_probabilities - the walkers over the same intervals are merged
let mut log_prob: Vec<_> = self.walker
.par_chunks(self.chunk_size.get())
.map(get_merged_refined_walker_prob)
.collect();
log_prob
.par_iter_mut()
.for_each(|v|
{
subtract_max(v);
}
);
// get the derivative, for merging later
let derivatives: Vec<_> = log_prob.par_iter()
.map(|v| derivative_merged(v))
.collect();
let hists: Vec<_> = self.walker
.iter()
.step_by(self.chunk_size.get())
.map(|w| w.hist())
.collect();
let e_hist = Hist::encapsulating_hist(&hists)?;
let alignment = hists.iter()
.zip(hists.iter().skip(1))
.map(|(&left, &right)| left.align(right))
.collect::<Result<Vec<_>, _>>()?;
let merge_points: Vec<_> = calc_merge_points(&alignment, &derivatives);
Ok(
(
merge_points,
alignment,
log_prob,
e_hist
)
)
}
/// # Result of the simulations!
/// This is what we do the simulation for!
///
/// It returns the natural logarithm of the normalized (i.e. sum=1 within numerical precision) probability density and the
/// histogram, which contains the corresponding bins.
///
/// Fails if the internal histograms (intervals) do not align. Might fail if
/// there is no overlap between neighboring intervals
#[deprecated(since="0.2.0", note="will be removed in future releases. Use new method 'derivative_merged_log_prob_and_aligned' or consider using 'average_merged_log_probability_and_align' instead")]
#[allow(deprecated)]
pub fn merged_log_prob_rees(&self) -> Result<(Hist, Vec<f64>), HistErrors>
where Hist: HistogramCombine
{
let (mut log_prob, e_hist) = self.merged_log_probability_rees()?;
norm_ln_prob(&mut log_prob);
Ok((e_hist, log_prob))
}
fn get_glue_stats(&self) -> GlueStats
{
let stats = self.walker
.chunks(self.chunk_size.get())
.map(
|walker|
{
let mut missing_steps = 0;
let mut accepted = 0;
let mut rejected = 0;
let mut replica_exchanges = 0;
let mut proposed_replica_exchanges = 0;
for w in walker{
if !w.is_finished(){
let missing = w.step_threshold() - w.step_count();
if missing > missing_steps{
missing_steps = missing;
}
}
let r = w.rejected_markov_steps();
let a = w.step_count() - r;
rejected += r;
accepted += a;
replica_exchanges += w.replica_exchanges();
proposed_replica_exchanges += w.proposed_replica_exchanges();
}
IntervalSimStats{
sim_progress: SimProgress::MissingSteps(missing_steps),
interval_sim_type: SimulationType::REES,
rejected_steps: rejected,
accepted_steps: accepted,
replica_exchanges: Some(replica_exchanges),
proposed_replica_exchanges: Some(proposed_replica_exchanges),
merged_over_walkers: self.chunk_size
}
}
).collect();
let roundtrips = self.rees_roundtrip_iter().collect();
GlueStats { interval_stats: stats, roundtrips }
}
/// # Results of the simulation
///
/// This is what we do the simulation for!
///
/// It returns [Glued] which allows you to print out the merged probability density function.
/// It also allows you to switch the base of the logarithm and so on, have a look!
///
/// It will use an average based merging algorithm, i.e., it will try to align the intervals
/// and merge them by using the values obtained by averaging in log-space
///
/// ## Notes
/// Fails if the internal histograms (intervals) do not align. Might fail if
/// there is no overlap between neighboring intervals
pub fn average_merged_log_probability_and_align(&self)-> Result<Glued<Hist, Energy>, HistErrors>
where Hist: HistogramCombine
{
let (hists, log_probs) = self.get_log_prob_and_hists();
let mut res = average_merged_and_aligned(
log_probs, &hists, LogBase::BaseE
)?;
let stats = self.get_glue_stats();
res.set_stats(stats);
Ok(res)
}
/// # Results of the simulation
///
/// This is what we do the simulation for!
///
/// It returns [Glued] which allows you to print out the merged probability density function.
/// It also allows you to switch the base of the logarithm and so on, have a look!
///
/// It will use an derivative based merging algorithm, i.e., it will try to align the intervals
/// and merge them by looking at the derivatives of the probability density function.
/// It will search for the (merging-)point where the derivatives are the most similar to each other
/// and glue by using the values of one of the intervals before the merging point and the
/// other interval afterwards. This is repeated for every interval
///
/// ## Notes
/// Fails if the internal histograms (intervals) do not align. Might fail if
/// there is no overlap between neighboring intervals
pub fn derivative_merged_log_prob_and_aligned(&self) -> Result<Glued<Hist, Energy>, HistErrors>
where Hist: HistogramCombine + Histogram
{
let (hists, log_probs) = self.get_log_prob_and_hists();
let mut res = derivative_merged_and_aligned(log_probs, &hists, LogBase::BaseE)?;
let stats = self.get_glue_stats();
res.set_stats(stats);
Ok(res)
}
#[deprecated(since="0.2.0", note="will be removed in future releases. Use new method 'derivative_merged_log_prob_and_aligned' or consider using 'average_merged_log_probability_and_align' instead")]
#[allow(deprecated)]
fn merged_log_probability_rees(&self) -> Result<(Vec<f64>, Hist), HistErrors>
where Hist: HistogramCombine
{
let (merge_points, alignment, log_prob, e_hist) = self.merged_log_probability_helper()?;
Ok(
only_merged(
merge_points,
alignment,
log_prob,
e_hist
)
)
}
}
/// # Merge probability density of multiple rees simulations
/// * Will calculate the merged log (base e) probability density. Also returns the corresponding histogram.
/// * `rees` does not need to be sorted in any way
/// ## Errors
/// * will return `HistErrors::EmptySlice` if the `rees` slice is empty
/// * will return other HistErrors if the intervals have no overlap
pub fn merged_log_prob_rees<Extra, Ensemble, R, Hist, Energy, S, Res>(
rees: &[Rees<Extra, Ensemble, R, Hist, Energy, S, Res>]
) -> Result<(Vec<f64>, Hist), HistErrors>
where Hist: Histogram + HistogramVal<Energy> + HistogramCombine + Send + Sync,
Energy: PartialOrd
{
merged_log_prob_ignore(rees, &[])
}
/// # Merge probability density of multiple rees simulations
/// * very similar to [merged_log_prob](`crate::rees::Rees::merged_log_prob`)
///
/// The difference is, that this function will ignore the specified walkers,
/// therefore `ignore` should be a slice of indices, which are to be ignored.
/// The slice does not have to be sorted in any way, though duplicate indices
/// and indices which are out of bounds will be ignored for the ignore list
pub fn merged_log_prob_ignore<Extra, Ensemble, R, Hist, Energy, S, Res>(
rees: &[Rees<Extra, Ensemble, R, Hist, Energy, S, Res>],
ignore: &[usize]
) -> Result<(Vec<f64>, Hist), HistErrors>
where Hist: HistogramVal<Energy> + HistogramCombine + Histogram + Send + Sync,
Energy: PartialOrd
{
if rees.is_empty() {
return Err(HistErrors::EmptySlice);
}
let merged_prob = merged_probs(rees);
let mut container = combine_container(rees, &merged_prob);
ignore_fn(&mut container, ignore);
let (merge_points, alignment, log_prob, e_hist) = align(&container)?;
Ok(
only_merged(
merge_points,
alignment,
log_prob,
e_hist
)
)
}
/// # Merge probability density of multiple rees simulations
/// * Will calculate the merged log (base 10) probability density. Also returns the corresponding histogram.
/// * If an interval has multiple walkers, their probability will be merged before all probabilities are aligned
/// * `rees` does not need to be sorted in any way
/// ## Errors
/// * will return `HistErrors::EmptySlice` if the `rees` slice is empty
/// * will return other HistErrors if the intervals have no overlap
pub fn merged_log10_prob_rees<Extra, Ensemble, R, Hist, Energy, S, Res>(
rees: &[Rees<Extra, Ensemble, R, Hist, Energy, S, Res>]
) -> Result<(Vec<f64>, Hist), HistErrors>
where Hist: Histogram + HistogramVal<Energy> + HistogramCombine + Send + Sync,
Energy: PartialOrd
{
let mut res = merged_log_prob_rees(rees)?;
ln_to_log10(&mut res.0);
Ok(res)
}
fn combine_container<'a, Ensemble, R, Hist, Energy, S, Res, Extra>(rees: &'a [Rees<Extra, Ensemble, R, Hist, Energy, S, Res>], merged_probs: &'a [Vec<f64>]) -> Vec<(&'a [f64], &'a Hist)>
where Hist: HistogramVal<Energy> + HistogramCombine,
Energy: PartialOrd
{
let hists: Vec<_> = rees.iter()
.flat_map(
|r|
{
r.walkers()
.iter()
.step_by(r.walkers_per_interval().get())
.map(|w| w.hist())
}
).collect();
assert_eq!(hists.len(), merged_probs.len());
let mut container: Vec<_> = merged_probs.iter()
.zip(hists)
.map(|(prob, hist)| (prob.as_slice(), hist))
.collect();
container
.sort_unstable_by(
|a, b|
{
a.1.first_border()
.partial_cmp(&b.1.first_border())
.unwrap_or(Ordering::Equal)
}
);
container
}
fn merged_probs<Ensemble, R, Hist, Energy, S, Res, Extra>(rees: &[Rees<Extra, Ensemble, R, Hist, Energy, S, Res>]) -> Vec<Vec<f64>>
where Hist: Histogram
{
let merged_probs: Vec<_> = rees.iter()
.flat_map(
|rees|
{
rees.walkers()
.chunks(rees.walkers_per_interval().get())
.map(get_merged_walker_prob)
}
).collect();
merged_probs
}
fn get_merged_walker_prob<R, Hist, Energy, S, Res>(walker: &[ReesWalker<R, Hist, Energy, S, Res>]) -> Vec<f64>
where Hist: Histogram
{
let log_len = walker[0].log_density().len();
debug_assert!(
walker.iter()
.all(|w| w.log_density().len() == log_len)
);
let mut averaged_log_density = walker[0].log_density_refined();
norm_ln_prob(&mut averaged_log_density);
if walker.len() > 1 {
walker[1..].iter()
.for_each(
|w|
{
let mut density = w.log_density_refined();
norm_ln_prob(&mut density);
averaged_log_density.iter_mut()
.zip(density)
.for_each(
|(average, other)|
{
*average += other;
}
)
}
);
let number_of_walkers = walker.len() as f64;
averaged_log_density.iter_mut()
.for_each(|average| *average /= number_of_walkers);
}
averaged_log_density
}