1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
use{
crate::{*, traits::*},
rand::Rng,
num_traits::{Bounded, ops::wrapping::*, identities::*},
std::{marker::PhantomData, io::Write, num::*}
};
#[cfg(feature = "serde_support")]
use serde::{Serialize, Deserialize};
/// # The 1/t Wang Landau approach comes from this paper
/// > R. E. Belardinelli and V. D. Pereyra,
/// > “Fast algorithm to calculate density of states,”
/// > Phys. Rev. E **75**: 046701 (2007), DOI [10.1103/PhysRevE.75.046701](https://doi.org/10.1103/PhysRevE.75.046701)
///
/// * The original Wang Landau algorithm comes from this paper
/// > F. Wang and D. P. Landau,
/// > “Efficient, multiple-range random walk algorithm to calculate the density of states,”
/// > Phys. Rev. Lett. **86**, 2050–2053 (2001), DOI [10.1103/PhysRevLett.86.2050](https://doi.org/10.1103/PhysRevLett.86.2050)
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde_support", derive(Serialize, Deserialize))]
pub struct WangLandau1T<Hist, Rng, Ensemble, S, Res, Energy>{
pub(crate) ensemble: Ensemble,
pub(crate) rng: Rng,
pub(crate) marker_res: PhantomData<Res>,
pub(crate) steps: Vec<S>,
mode: WangLandauMode,
pub(crate) log_density: Vec<f64>,
pub(crate) log_f: f64,
pub(crate) log_f_threshold: f64,
pub(crate) step_size: usize,
step_count: usize,
accepted_steps_total: usize,
recected_steps_total: usize,
accepted_steps_current: usize,
recected_steps_current: usize,
pub(crate) old_bin: usize,
pub(crate) hist: Hist,
pub(crate) old_energy: Option<Energy>,
check_refine_every: usize,
}
impl<Hist, R, E, S, Res, Energy> GlueAble<Hist> for WangLandau1T<Hist, R, E, S, Res, Energy>
where Hist: Clone
{
fn push_glue_entry_ignoring(
&self,
job: &mut GlueJob<Hist>,
ignore_idx: &[usize]
) {
if !ignore_idx.contains(&0)
{
let sim_progress = SimProgress::LogF(self.log_f);
let rejected = self.total_steps_rejected() as u64;
let accepted = self.total_steps_accepted() as u64;
let stats = IntervalSimStats{
sim_progress,
interval_sim_type: SimulationType::WangLandau1T,
rejected_steps: rejected,
accepted_steps: accepted,
replica_exchanges: None,
proposed_replica_exchanges: None,
merged_over_walkers: NonZeroUsize::new(1).unwrap()
};
let glue_entry = GlueEntry{
hist: self.hist.clone(),
prob: self.log_density.clone(),
log_base: LogBase::BaseE,
interval_stats: stats
};
job.collection.push(glue_entry);
}
}
}
impl<Hist, Rng, Ensemble, S, Res, Energy>WangLandau1T<Hist, Rng, Ensemble, S, Res, Energy>
{
/// Returns internal ensemble, histogram and Rng
pub fn into_inner(self) -> (Ensemble, Hist, Rng)
{
(self.ensemble, self.hist, self.rng)
}
}
impl<Hist, R, E, S, Res, Energy> WangLandau
for WangLandau1T<Hist, R, E, S, Res, Energy>
{
#[inline(always)]
fn log_f(&self) -> f64
{
self.log_f
}
#[inline(always)]
fn log_f_threshold(&self) -> f64
{
self.log_f_threshold
}
fn set_log_f_threshold(&mut self, log_f_threshold: f64) -> Result<f64, WangLandauErrors>
{
if !log_f_threshold.is_finite() || log_f_threshold.is_sign_negative() {
return Err(WangLandauErrors::InvalidLogFThreshold);
}
let old_threshold = self.log_f_threshold;
self.log_f_threshold = log_f_threshold;
Ok(old_threshold)
}
#[inline(always)]
fn log_density(&self) -> &Vec<f64>
{
&self.log_density
}
fn write_log<W: Write>(&self, mut writer: W) -> Result<(), std::io::Error> {
writeln!(writer,
"#Acceptance prob_total: {}\n#Acceptance prob current: {}\n#total_steps: {}\n#log_f: {:e}\n#Current_mode {:?}",
self.fraction_accepted_total(),
self.fraction_accepted_current(),
self.step_counter(),
self.log_f(),
self.mode
)?;
writeln!(
writer,
"#total_steps_accepted: {}\n#total_steps_rejected: {}\n#current_accepted_steps: {}\n#current_rejected_steps: {}",
self.accepted_steps_total,
self.recected_steps_total,
self.accepted_steps_current,
self.recected_steps_current
)
}
#[inline(always)]
fn mode(&self) -> WangLandauMode
{
self.mode
}
#[inline(always)]
fn step_counter(&self) -> usize
{
self.step_count
}
#[inline(always)]
fn total_steps_rejected(&self) -> usize {
self.recected_steps_total
}
#[inline(always)]
fn total_steps_accepted(&self) -> usize {
self.accepted_steps_total
}
}
impl<Hist, R, E, S, Res, Energy>
WangLandau1T<Hist, R, E, S, Res, Energy>
where
Hist: Histogram + HistogramVal<Energy>
{
/// # Check if `self` is initialized
/// * if this returns true, you can begin the WangLandau simulation
/// * otherwise call one of the `self.init*` methods
pub fn is_initialized(&self) -> bool
{
match &self.old_energy{
None => false,
Some(e) => {
self.hist.is_inside(e)
}
}
}
}
/// Possible errors when setting initial guess
#[derive(Clone, Copy, Debug)]
pub enum SetInitialError{
/// # Dimensions do not match!
/// The length of the initial guess and the amount of bins have to be the same
DimensionError,
/// All values inside the initial guess have to be finite
NonFiniteEncountered,
/// log_f has to fullfill 0.0 < log_f < 10.0
InvalidLogF
}
impl <Hist, R, E, S, Res, Energy> WangLandauEnsemble<E>
for WangLandau1T<Hist, R, E, S, Res, Energy>
{
#[inline(always)]
fn ensemble(&self) -> &E {
&self.ensemble
}
#[inline(always)]
unsafe fn ensemble_mut(&mut self) -> &mut E {
&mut self.ensemble
}
}
impl <Hist, R, E, S, Res, Energy> WangLandauEnergy<Energy>
for WangLandau1T<Hist, R, E, S, Res, Energy>
{
#[inline(always)]
fn energy(&self) -> Option<&Energy> {
self.old_energy.as_ref()
}
}
impl <Hist, R, E, S, Res, Energy> WangLandauHist<Hist>
for WangLandau1T<Hist, R, E, S, Res, Energy>
{
#[inline(always)]
fn hist(&self) -> &Hist {
&self.hist
}
}
impl<Hist, R, E, S, Res, Energy>
WangLandau1T<Hist, R, E, S, Res, Energy>
{
/// # Acceptance rate
/// Fraction of performed wang landau steps, that were accepted
fn fraction_accepted_total(&self) -> f64
{
let sum = self.accepted_steps_total + self.recected_steps_total;
self.accepted_steps_total as f64 / sum as f64
}
/// # Acceptance rate since last Refinement
/// Fraction of performed wang landau steps since
/// the last time, the factor f was refined, that were accepted
fn fraction_accepted_current(&self) -> f64
{
let total = self.accepted_steps_current + self.recected_steps_current;
if total == 0 {
f64::NAN
} else {
self.accepted_steps_current as f64 / total as f64
}
}
/// # Set the initial guess for the non-normalized probability estimate
/// * `new_guess` your new guess for the probability estimate. Its length has to equal the number of bins of the internal histogram
/// which is the same as the length of the old estimate which you can get by calling [log_density](Self::log_density). All contained values have
/// to be finite
/// * `new_log_f`: Which log_f to start at? 0.0 < log_f <= 10.0 has to be true.
/// If you don't know what's best I recommand starting with log_f=1.0, the better your probability estimate is, the smaller this value can be
/// # Note
/// This will reset the calculation. Meaning you will have to call one of the initializing functions like `init_greedy_heuristic`again
/// and all internal counters are reset to 0
pub fn set_initial_probability_guess(mut self, new_guess: Vec<f64>, new_log_f: f64) -> Result<Self, SetInitialError>
where Hist: Histogram
{
if 0.0 >= new_log_f || new_log_f > 10.0 {
Err(SetInitialError::InvalidLogF)
}
else if new_guess.len() != self.log_density.len()
{
Err(SetInitialError::DimensionError)
} else if new_guess.iter().any(|val| !val.is_finite())
{
Err(SetInitialError::NonFiniteEncountered)
} else {
self.log_density = new_guess;
self.log_f = new_log_f;
self.step_count = 0;
self.accepted_steps_current = 0;
self.accepted_steps_total = 0;
self.recected_steps_current = 0;
self.recected_steps_total = 0;
self.mode = WangLandauMode::RefineOriginal;
self.hist.reset();
self.old_energy = None;
self.old_bin = usize::MAX;
Ok(self)
}
}
}
impl<Hist, R, E, S, Res, Energy>
WangLandau1T<Hist, R, E, S, Res, Energy>
where
R: Rng,
E: MarkovChain<S, Res>,
Energy: Clone,
Hist: Histogram + HistogramVal<Energy>
{
/// # Create a new WangLandau simulation
/// **IMPORTANT** You have to call one of the `init*` functions,
/// to create a valid state, before you can start the simulation
/// ## Parameter
/// * `log_f_threshold`: how small should the ln(f) (see paper) become
/// until the simulation is finished?
/// * `ensemble`: The ensemble to explore.
/// Current state of ensemble will be used as inital condition for the `init*` functions
/// * `step_size`: The markov steps will be performed with this step size, e.g.,
/// `ensemble.m_steps(step_size)`
/// * `histogram`: Provides the binning. You can either use one of the already implemented
/// histograms, like `HistU32Fast`, `HistU32`, `HistF64` etc. or implement your own by
/// implementing the traits `Histogram + HistogramVal<Energy>` yourself
/// * `check_refine_every`: how often to check, if every bin in the histogram was hit.
/// Needs to be at least 1. Good values depend on the problem at hand, but if you are
/// unsure, you can start with a value like 1000
pub fn new(
log_f_threshold: f64,
ensemble: E,
rng: R,
step_size: usize,
histogram: Hist,
check_refine_every: usize
)-> Result<Self, WangLandauErrors>
{
if !log_f_threshold.is_finite() || log_f_threshold.is_sign_negative()
{
return Err(WangLandauErrors::InvalidLogFThreshold);
}
else if check_refine_every == 0 {
return Err(WangLandauErrors::CheckRefineEvery0)
}
let log_density = vec![0.0; histogram.bin_count()];
let steps = Vec::with_capacity(step_size);
Ok(
Self{
ensemble,
step_count: 0,
step_size,
hist: histogram,
rng,
marker_res: PhantomData::<Res>,
log_f: 1.0,
log_density,
log_f_threshold,
mode: WangLandauMode::RefineOriginal,
recected_steps_current: 0,
recected_steps_total: 0,
accepted_steps_current: 0,
accepted_steps_total: 0,
old_bin: usize::MAX,
old_energy: None,
check_refine_every,
steps,
}
)
}
fn init<F>(
&mut self,
energy_fn: F,
step_limit: Option<u64>
) -> Result<(), WangLandauErrors>
where F: Fn(&mut E) -> Option<Energy>
{
self.old_energy = energy_fn(&mut self.ensemble);
if self.old_energy.is_some(){
return Ok(());
}
match step_limit {
None => {
loop {
self.ensemble.m_steps_quiet(self.step_size);
self.old_energy = energy_fn(&mut self.ensemble);
if self.old_energy.is_some(){
self.count_accepted();
return Ok(());
}
self.count_rejected();
}
},
Some(limit) => {
for _ in 0..limit {
self.ensemble.m_steps_quiet(self.step_size);
self.old_energy = energy_fn(&mut self.ensemble);
if self.old_energy.is_some(){
self.count_accepted();
return Ok(());
}
self.count_rejected();
}
Err(WangLandauErrors::InitFailed)
}
}
}
fn greedy_helper<F, H, J>(
&mut self,
old_distance: &mut J,
energy_fn: F,
distance_fn: H,
) where F: Fn(&mut E) -> Option<Energy> + Copy,
H: Fn(&Hist, &Energy) -> J,
J: PartialOrd
{
self.ensemble
.m_steps(self.step_size, &mut self.steps);
if let Some(energy) = energy_fn(&mut self.ensemble) {
let distance = distance_fn(&self.hist, &energy);
if distance <= *old_distance {
self.old_energy = Some(energy);
*old_distance = distance;
self.count_accepted();
return;
}
}
self.count_rejected();
self.ensemble
.undo_steps_quiet(&self.steps);
}
/// # Find a valid starting Point
/// * if the ensemble is already at a valid starting point,
/// the ensemble is left unchanged (as long as your energy calculation does not change the ensemble)
/// * Uses a greedy heuristik. Performs markov steps. If that brought us closer to the target interval,
/// the step is accepted. Otherwise it is rejected
/// # Parameter
/// * `step_limit`: Some(val) -> val is max number of steps tried, if no valid state is found, it will return an Error. None -> will loop until either
/// a valid state is found or forever
/// * `energy_fn` function calculating `Some(energy)` of the system
/// or rather the Parameter of which you wish to obtain the probability distribution.
/// Has to be the same function as used for the wang landau simulation later.
/// 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)` is `None`
/// will always be rejected
pub fn init_greedy_heuristic<F>(
&mut self,
energy_fn: F,
step_limit: Option<u64>,
) -> Result<(), WangLandauErrors>
where F: Fn(&mut E) -> Option<Energy>,
{
self.init(&energy_fn, step_limit)?;
let mut old_distance = self.hist
.distance(self.old_energy_ref());
let mut step_count = 0;
while old_distance != 0.0 {
self.greedy_helper(
&mut old_distance,
&energy_fn,
|hist, energy| {
hist.distance(energy)
}
);
if let Some(limit) = step_limit {
if limit == step_count{
return Err(WangLandauErrors::InitFailed);
}
step_count += 1;
}
}
self.end_init();
Ok(())
}
/// # Find a valid starting Point
/// * if the ensemble is already at a valid starting point,
/// the ensemble is left unchanged (as long as your energy calculation does not change the ensemble)
/// * Uses overlapping intervals. Accepts a step, if the resulting ensemble is in the same interval as before,
/// or it is in an interval closer to the target interval
/// * Take a look at the [`HistogramIntervalDistance` trait](`crate::HistogramIntervalDistance`)
/// # Parameter
/// * `step_limit`: Some(val) -> val is max number of steps tried, if no valid state is found, it will return an Error. None -> will loop until either
/// a valid state is found or forever
/// * `energy_fn` function calculating `Some(energy)` of the system
/// or rather the Parameter of which you wish to obtain the probability distribution.
/// Has to be the same function as used for the wang landau simulation later.
/// 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)` is `None`
/// will always be rejected
pub fn init_interval_heuristik<F>(
&mut self,
overlap: NonZeroUsize,
energy_fn: F,
step_limit: Option<u64>,
) -> Result<(), WangLandauErrors>
where F: Fn(&mut E) -> Option<Energy>,
Hist: HistogramIntervalDistance<Energy>
{
self.init(&energy_fn, step_limit)?;
let mut old_dist = self.hist
.interval_distance_overlap(
self.old_energy_ref(),
overlap
);
let dist = |h: &Hist, val: &Energy| h.interval_distance_overlap(val, overlap);
let mut step_count = 0;
while old_dist != 0 {
self.greedy_helper(
&mut old_dist,
&energy_fn,
dist
);
if let Some(limit) = step_limit {
if limit == step_count{
return Err(WangLandauErrors::InitFailed);
}
step_count += 1;
}
}
self.end_init();
Ok(())
}
/// # Find a valid starting Point
/// * if the ensemble is already at a valid starting point,
/// the ensemble is left unchanged (as long as your energy calculation does not change the ensemble)
/// * `overlap` - see [`HistogramIntervalDistance` trait](`crate::HistogramIntervalDistance`)
/// Should be greater than 0 and smaller than the number of bins in your histogram. E.g. `overlap = 3` if you have 200 bins
/// * `mid` - should be something like `128u8`, `0i8` or `0i16`. It is very unlikely that using a type with more than 16 bit makes sense for mid
/// * `step_limit`: Some(val) -> val is max number of steps tried, if no valid state is found, it will return an Error. None -> will loop until either
/// a valid state is found or forever
/// * alternates between greedy and interval heuristik everytime a wrapping counter passes `mid` or `U::min_value()`
/// * I recommend using this heuristik, if you do not know which one to use
/// # Parameter
/// * `energy_fn` function calculating `Some(energy)` of the system
/// or rather the Parameter of which you wish to obtain the probability distribution.
/// Has to be the same function as used for the wang landau simulation later.
/// 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)` is `None`
/// will always be rejected
pub fn init_mixed_heuristik<F, U>
(
&mut self,
overlap: NonZeroUsize,
mid: U,
energy_fn: F,
step_limit: Option<u64>
) -> Result<(), WangLandauErrors>
where F: Fn(&mut E) -> Option<Energy>,
Hist: HistogramIntervalDistance<Energy>,
U: One + Bounded + WrappingAdd + Eq + PartialOrd
{
self.init(&energy_fn, step_limit)?;
if self.hist
.is_inside(
self.old_energy_ref()
)
{
self.end_init();
return Ok(());
}
let mut old_dist = f64::INFINITY;
let mut old_dist_interval = usize::MAX;
let mut counter: U = U::min_value();
let min_val = U::min_value();
let one = U::one();
let dist_interval = |h: &Hist, val: &Energy| h.interval_distance_overlap(val, overlap);
let mut step_count = 0;
loop {
if counter == min_val {
let current_energy = self.old_energy_ref();
old_dist = self.hist.distance(current_energy);
}else if counter == mid {
let current_energy = self.old_energy_ref();
old_dist_interval = dist_interval(&self.hist, current_energy);
}
if counter < mid {
self.greedy_helper(
&mut old_dist,
&energy_fn,
|hist, val| {
hist.distance(val)
}
);
if old_dist == 0.0 {
break;
}
} else {
self.greedy_helper(
&mut old_dist_interval,
&energy_fn,
dist_interval,
);
if old_dist_interval == 0 {
break;
}
}
counter = counter.wrapping_add(&one);
if let Some(limit) = step_limit {
if limit == step_count{
return Err(WangLandauErrors::InitFailed);
}
step_count += 1;
}
}
self.end_init();
Ok(())
}
fn end_init(&mut self)
{
self.old_bin = self.hist
.get_bin_index(
self.old_energy_ref()
).expect("Error in heuristic - old bin invalid");
}
fn old_energy_clone(&self) -> Energy {
self.old_energy_ref()
.clone()
}
fn old_energy_ref(&self) -> &Energy {
self.old_energy
.as_ref()
.unwrap()
}
fn count_accepted(&mut self){
self.ensemble.steps_accepted(&self.steps);
self.accepted_steps_current += 1;
self.accepted_steps_total += 1;
}
fn count_rejected(&mut self){
self.ensemble.steps_rejected(&self.steps);
self.recected_steps_current += 1;
self.recected_steps_total += 1;
}
fn check_refine(&mut self)
{
match self.mode{
WangLandauMode::Refine1T => {
self.log_f = self.log_f_1_t();
},
WangLandauMode::RefineOriginal => {
if self.step_count % self.check_refine_every == 0
&& !self.hist.any_bin_zero()
{
self.recected_steps_current = 0;
self.accepted_steps_current = 0;
let ref_1_t = self.log_f_1_t();
self.log_f *= 0.5;
if self.log_f < ref_1_t {
self.log_f = ref_1_t;
self.mode = WangLandauMode::Refine1T;
}
self.hist.reset();
}
}
}
}
fn wl_step_helper(&mut self, energy: Option<Energy>)
{
let current_energy = match energy
{
Some(energy) => energy,
None => {
self.count_rejected();
self.hist.count_index(self.old_bin).unwrap();
self.log_density[self.old_bin] += self.log_f;
self.ensemble.undo_steps_quiet(&self.steps);
return;
}
};
match self.hist.get_bin_index(¤t_energy)
{
Ok(current_bin) => {
let accept_prob = self.metropolis_acception_prob( current_bin);
if self.rng.gen::<f64>() > accept_prob {
// reject step
self.count_rejected();
self.ensemble.undo_steps_quiet(&self.steps);
} else {
// accept step
self.count_accepted();
self.old_energy = Some(current_energy);
self.old_bin = current_bin;
}
},
_ => {
// invalid step -> reject
self.count_rejected();
self.ensemble.undo_steps_quiet(&self.steps);
}
};
self.hist.count_index(self.old_bin).unwrap();
self.log_density[self.old_bin] += self.log_f;
}
/// # Wang Landau Step
/// * performs a single Wang Landau step
/// # Parameter
/// * `energy_fn` function calculating `Some(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)` is `None`
/// will always be rejected
/// # Important
/// * You have to call one of the `self.init*` functions before calling this one -
/// **will panic otherwise**
pub fn wang_landau_step<F>(
&mut self,
energy_fn: F,
)where F: Fn(&E) -> Option<Energy>
{
unsafe {
self.wang_landau_step_unsafe(|e| energy_fn(e))
}
}
/// # Wang Landau Step
/// * if possible, use `self.wang_landau_step()` instead - it is safer
/// * performs a single Wang Landau step
/// # Parameter
/// * `energy_fn` function calculating `Some(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)` is `None`
/// will always be rejected
/// # Safety
/// * You have to call one of the `self.init*` functions before calling this one -
/// **will panic otherwise**
/// * unsafe, because you have to make sure, that the `energy_fn` function
/// does not change the state of the ensemble in such a way, that the result of
/// `energy_fn` changes when called again. Maybe do cleanup at the beginning of the energy function?
pub unsafe fn wang_landau_step_unsafe<F>(
&mut self,
mut energy_fn: F,
)where F: FnMut(&mut E) -> Option<Energy>
{
debug_assert!(
self.old_energy.is_some(),
"Error - self.old_energy invalid - Did you forget to call one of the `self.init*` members for initialization?"
);
self.step_count += 1;
self.ensemble.m_steps(self.step_size, &mut self.steps);
self.check_refine();
let current_energy = energy_fn(&mut self.ensemble);
self.wl_step_helper(current_energy);
}
/// # Wang Landau Step
/// * performs a single Wang Landau step
/// # Parameter
/// * `energy_fn` function calculating the energy of the system **on the fly**
/// * **steps resulting in invalid ensembles are not allowed!**
/// # Important
/// * You have to call one of the `self.init*` functions before calling this one -
/// **will panic otherwise**
pub fn wang_landau_step_acc<F>(
&mut self,
energy_fn: F,
)
where F: FnMut(&E, &S, &mut Energy)
{
debug_assert!(
self.old_energy.is_some(),
"Error - self.old_energy invalid - Did you forget to call one of the `self.init*` members for initialization?"
);
self.step_count += 1;
let mut new_energy = self.old_energy_clone();
self.ensemble
.m_steps_acc(
self.step_size,
&mut self.steps,
&mut new_energy,
energy_fn
);
self.check_refine();
self.wl_step_helper(Some(new_energy));
}
/// # Wang Landau
/// * perform Wang Landau simulation
/// * calls `self.wang_landau_step(energy_fn, valid_ensemble)` until `self.is_finished()`
pub fn wang_landau_convergence<F>(
&mut self,
energy_fn: F,
)where F: Fn(&E) -> Option<Energy>,
{
while !self.is_finished() {
self.wang_landau_step(&energy_fn);
}
}
/// # Wang Landau - efficient energy calculation
/// * perform Wang Landau simulation
/// * calls `self.wang_landau_step_acc(energy_fn, valid_ensemble)` until `self.is_finished()`
pub fn wang_landau_convergence_acc<F>(
&mut self,
mut energy_fn: F,
)where F: FnMut(&E, &S, &mut Energy)
{
while !self.is_finished() {
self.wang_landau_step_acc(&mut energy_fn);
}
}
/// # Wang Landau
/// * if possible, use `self.wang_landau_convergence()` instead - it is safer
/// * perform Wang Landau simulation
/// * calls `self.wang_landau_step_unsafe(energy_fn, valid_ensemble)` until `self.is_finished()`
/// # Safety
/// * You have mutable access to your ensemble, which is why this function is unsafe.
/// If you do anything, which changes the future outcome of the energy function, the results will be wrong!
/// I use the unsafe keyword here to force the user to acknowledge that.
pub unsafe fn wang_landau_convergence_unsafe<F>(
&mut self,
mut energy_fn: F,
)where F: FnMut(&mut E) -> Option<Energy>,
{
while !self.is_finished() {
self.wang_landau_step_unsafe(&mut energy_fn);
}
}
/// # Wang Landau
/// * perform Wang Landau simulation
/// * calls `self.wang_landau_step(energy_fn)` until `self.is_finished()`
/// or `condition(&self)` is false
pub fn wang_landau_while<F, W>(
&mut self,
energy_fn: F,
mut condition: W
) where F: Fn(&E) -> Option<Energy>,
W: FnMut(&Self) -> bool,
{
while !self.is_finished() && condition(self) {
self.wang_landau_step(&energy_fn);
}
}
/// # Wang Landau
/// * perform Wang Landau simulation
/// * calls `self.wang_landau_step(energy_fn)` until `self.is_finished()`
/// or `condition(&self)` is false
pub fn wang_landau_while_acc<F, W>(
&mut self,
mut energy_fn: F,
mut condition: W
) where F: FnMut(&E, &S, &mut Energy),
W: FnMut(&Self) -> bool,
{
while !self.is_finished() && condition(self) {
self.wang_landau_step_acc(&mut energy_fn);
}
}
/// # Wang Landau
/// * if possible, use `self.wang_landau_while()` instead - it is safer
/// * perform Wang Landau simulation
/// * calls `self.wang_landau_step(energy_fn)` until `self.is_finished()`
/// or `condition(&self)` is false
/// # Safety
/// * You have mutable access to your ensemble, which is why this function is unsafe.
/// If you do anything, which changes the future outcome of the energy function, the results will be wrong!
/// I use the unsafe keyword here to force the user to acknowledge that
pub unsafe fn wang_landau_while_unsafe<F, W>(
&mut self,
mut energy_fn: F,
mut condition: W
) where F: FnMut(&mut E) -> Option<Energy>,
W: FnMut(&Self) -> bool,
{
while !self.is_finished() && condition(self) {
self.wang_landau_step_unsafe(&mut energy_fn);
}
}
/// **panics** if index is invalid
#[inline(always)]
fn metropolis_acception_prob(&self, new_bin: usize) -> f64
{
(self.log_density[self.old_bin] - self.log_density[new_bin])
.exp()
}
}
#[cfg(test)]
mod tests {
use super::*;
use rand_pcg::Pcg64Mcg;
use rand::SeedableRng;
use crate::examples::coin_flips::*;
#[test]
#[cfg_attr(miri,ignore)]
fn wl_simulations_equal() {
let mut rng = Pcg64Mcg::seed_from_u64(2239790);
let ensemble = CoinFlipSequence::new(100, Pcg64Mcg::from_rng(&mut rng).unwrap());
let histogram = HistogramFast::new_inclusive(0, 100).unwrap();
let mut wl= WangLandau1T::new(
0.0075,
ensemble,
rng,
1,
histogram,
30
).unwrap();
wl.init_mixed_heuristik(
NonZeroUsize::new(3).unwrap(),
6400i16,
|e| {
Some(e.head_count())
},
None
).unwrap();
let mut wl_backup = wl.clone();
let start_wl= std::time::Instant::now();
wl.wang_landau_convergence(
|e| Some(e.head_count())
);
let dur_1 = start_wl.elapsed();
let start_wl_acc = std::time::Instant::now();
wl_backup.wang_landau_convergence_acc(
CoinFlipSequence::update_head_count
);
let dur_2 = start_wl_acc.elapsed();
println!("WL: {}, WL_ACC: {}, difference: {}", dur_1.as_nanos(), dur_2.as_nanos(), dur_1.as_nanos() - dur_2.as_nanos());
// assert, that the different methods lead to the same result
for (&log_value, &log_value_acc) in wl.log_density().iter().zip(wl_backup.log_density().iter()){
assert_eq!(log_value, log_value_acc);
}
}
}