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-rw-r--r--rand/src/distributions/weighted/alias_method.rs499
-rw-r--r--rand/src/distributions/weighted/mod.rs363
2 files changed, 0 insertions, 862 deletions
diff --git a/rand/src/distributions/weighted/alias_method.rs b/rand/src/distributions/weighted/alias_method.rs
deleted file mode 100644
index bdd4ba0..0000000
--- a/rand/src/distributions/weighted/alias_method.rs
+++ /dev/null
@@ -1,499 +0,0 @@
-//! This module contains an implementation of alias method for sampling random
-//! indices with probabilities proportional to a collection of weights.
-
-use super::WeightedError;
-#[cfg(not(feature = "std"))]
-use crate::alloc::vec::Vec;
-#[cfg(not(feature = "std"))]
-use crate::alloc::vec;
-use core::fmt;
-use core::iter::Sum;
-use core::ops::{Add, AddAssign, Div, DivAssign, Mul, MulAssign, Sub, SubAssign};
-use crate::distributions::uniform::SampleUniform;
-use crate::distributions::Distribution;
-use crate::distributions::Uniform;
-use crate::Rng;
-
-/// A distribution using weighted sampling to pick a discretely selected item.
-///
-/// Sampling a [`WeightedIndex<W>`] distribution returns the index of a randomly
-/// selected element from the vector used to create the [`WeightedIndex<W>`].
-/// The chance of a given element being picked is proportional to the value of
-/// the element. The weights can have any type `W` for which a implementation of
-/// [`Weight`] exists.
-///
-/// # Performance
-///
-/// Given that `n` is the number of items in the vector used to create an
-/// [`WeightedIndex<W>`], [`WeightedIndex<W>`] will require `O(n)` amount of
-/// memory. More specifically it takes up some constant amount of memory plus
-/// the vector used to create it and a [`Vec<u32>`] with capacity `n`.
-///
-/// Time complexity for the creation of a [`WeightedIndex<W>`] is `O(n)`.
-/// Sampling is `O(1)`, it makes a call to [`Uniform<u32>::sample`] and a call
-/// to [`Uniform<W>::sample`].
-///
-/// # Example
-///
-/// ```
-/// use rand::distributions::weighted::alias_method::WeightedIndex;
-/// use rand::prelude::*;
-///
-/// let choices = vec!['a', 'b', 'c'];
-/// let weights = vec![2, 1, 1];
-/// let dist = WeightedIndex::new(weights).unwrap();
-/// let mut rng = thread_rng();
-/// for _ in 0..100 {
-/// // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
-/// println!("{}", choices[dist.sample(&mut rng)]);
-/// }
-///
-/// let items = [('a', 0), ('b', 3), ('c', 7)];
-/// let dist2 = WeightedIndex::new(items.iter().map(|item| item.1).collect()).unwrap();
-/// for _ in 0..100 {
-/// // 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
-/// println!("{}", items[dist2.sample(&mut rng)].0);
-/// }
-/// ```
-///
-/// [`WeightedIndex<W>`]: crate::distributions::weighted::alias_method::WeightedIndex
-/// [`Weight`]: crate::distributions::weighted::alias_method::Weight
-/// [`Vec<u32>`]: Vec
-/// [`Uniform<u32>::sample`]: Distribution::sample
-/// [`Uniform<W>::sample`]: Distribution::sample
-pub struct WeightedIndex<W: Weight> {
- aliases: Vec<u32>,
- no_alias_odds: Vec<W>,
- uniform_index: Uniform<u32>,
- uniform_within_weight_sum: Uniform<W>,
-}
-
-impl<W: Weight> WeightedIndex<W> {
- /// Creates a new [`WeightedIndex`].
- ///
- /// Returns an error if:
- /// - The vector is empty.
- /// - The vector is longer than `u32::MAX`.
- /// - For any weight `w`: `w < 0` or `w > max` where `max = W::MAX /
- /// weights.len()`.
- /// - The sum of weights is zero.
- pub fn new(weights: Vec<W>) -> Result<Self, WeightedError> {
- let n = weights.len();
- if n == 0 {
- return Err(WeightedError::NoItem);
- } else if n > ::core::u32::MAX as usize {
- return Err(WeightedError::TooMany);
- }
- let n = n as u32;
-
- let max_weight_size = W::try_from_u32_lossy(n)
- .map(|n| W::MAX / n)
- .unwrap_or(W::ZERO);
- if !weights
- .iter()
- .all(|&w| W::ZERO <= w && w <= max_weight_size)
- {
- return Err(WeightedError::InvalidWeight);
- }
-
- // The sum of weights will represent 100% of no alias odds.
- let weight_sum = Weight::sum(weights.as_slice());
- // Prevent floating point overflow due to rounding errors.
- let weight_sum = if weight_sum > W::MAX {
- W::MAX
- } else {
- weight_sum
- };
- if weight_sum == W::ZERO {
- return Err(WeightedError::AllWeightsZero);
- }
-
- // `weight_sum` would have been zero if `try_from_lossy` causes an error here.
- let n_converted = W::try_from_u32_lossy(n).unwrap();
-
- let mut no_alias_odds = weights;
- for odds in no_alias_odds.iter_mut() {
- *odds *= n_converted;
- // Prevent floating point overflow due to rounding errors.
- *odds = if *odds > W::MAX { W::MAX } else { *odds };
- }
-
- /// This struct is designed to contain three data structures at once,
- /// sharing the same memory. More precisely it contains two linked lists
- /// and an alias map, which will be the output of this method. To keep
- /// the three data structures from getting in each other's way, it must
- /// be ensured that a single index is only ever in one of them at the
- /// same time.
- struct Aliases {
- aliases: Vec<u32>,
- smalls_head: u32,
- bigs_head: u32,
- }
-
- impl Aliases {
- fn new(size: u32) -> Self {
- Aliases {
- aliases: vec![0; size as usize],
- smalls_head: ::core::u32::MAX,
- bigs_head: ::core::u32::MAX,
- }
- }
-
- fn push_small(&mut self, idx: u32) {
- self.aliases[idx as usize] = self.smalls_head;
- self.smalls_head = idx;
- }
-
- fn push_big(&mut self, idx: u32) {
- self.aliases[idx as usize] = self.bigs_head;
- self.bigs_head = idx;
- }
-
- fn pop_small(&mut self) -> u32 {
- let popped = self.smalls_head;
- self.smalls_head = self.aliases[popped as usize];
- popped
- }
-
- fn pop_big(&mut self) -> u32 {
- let popped = self.bigs_head;
- self.bigs_head = self.aliases[popped as usize];
- popped
- }
-
- fn smalls_is_empty(&self) -> bool {
- self.smalls_head == ::core::u32::MAX
- }
-
- fn bigs_is_empty(&self) -> bool {
- self.bigs_head == ::core::u32::MAX
- }
-
- fn set_alias(&mut self, idx: u32, alias: u32) {
- self.aliases[idx as usize] = alias;
- }
- }
-
- let mut aliases = Aliases::new(n);
-
- // Split indices into those with small weights and those with big weights.
- for (index, &odds) in no_alias_odds.iter().enumerate() {
- if odds < weight_sum {
- aliases.push_small(index as u32);
- } else {
- aliases.push_big(index as u32);
- }
- }
-
- // Build the alias map by finding an alias with big weight for each index with
- // small weight.
- while !aliases.smalls_is_empty() && !aliases.bigs_is_empty() {
- let s = aliases.pop_small();
- let b = aliases.pop_big();
-
- aliases.set_alias(s, b);
- no_alias_odds[b as usize] = no_alias_odds[b as usize]
- - weight_sum
- + no_alias_odds[s as usize];
-
- if no_alias_odds[b as usize] < weight_sum {
- aliases.push_small(b);
- } else {
- aliases.push_big(b);
- }
- }
-
- // The remaining indices should have no alias odds of about 100%. This is due to
- // numeric accuracy. Otherwise they would be exactly 100%.
- while !aliases.smalls_is_empty() {
- no_alias_odds[aliases.pop_small() as usize] = weight_sum;
- }
- while !aliases.bigs_is_empty() {
- no_alias_odds[aliases.pop_big() as usize] = weight_sum;
- }
-
- // Prepare distributions for sampling. Creating them beforehand improves
- // sampling performance.
- let uniform_index = Uniform::new(0, n);
- let uniform_within_weight_sum = Uniform::new(W::ZERO, weight_sum);
-
- Ok(Self {
- aliases: aliases.aliases,
- no_alias_odds,
- uniform_index,
- uniform_within_weight_sum,
- })
- }
-}
-
-impl<W: Weight> Distribution<usize> for WeightedIndex<W> {
- fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
- let candidate = rng.sample(self.uniform_index);
- if rng.sample(&self.uniform_within_weight_sum) < self.no_alias_odds[candidate as usize] {
- candidate as usize
- } else {
- self.aliases[candidate as usize] as usize
- }
- }
-}
-
-impl<W: Weight> fmt::Debug for WeightedIndex<W>
-where
- W: fmt::Debug,
- Uniform<W>: fmt::Debug,
-{
- fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
- f.debug_struct("WeightedIndex")
- .field("aliases", &self.aliases)
- .field("no_alias_odds", &self.no_alias_odds)
- .field("uniform_index", &self.uniform_index)
- .field("uniform_within_weight_sum", &self.uniform_within_weight_sum)
- .finish()
- }
-}
-
-impl<W: Weight> Clone for WeightedIndex<W>
-where
- Uniform<W>: Clone,
-{
- fn clone(&self) -> Self {
- Self {
- aliases: self.aliases.clone(),
- no_alias_odds: self.no_alias_odds.clone(),
- uniform_index: self.uniform_index.clone(),
- uniform_within_weight_sum: self.uniform_within_weight_sum.clone(),
- }
- }
-}
-
-/// Trait that must be implemented for weights, that are used with
-/// [`WeightedIndex`]. Currently no guarantees on the correctness of
-/// [`WeightedIndex`] are given for custom implementations of this trait.
-pub trait Weight:
- Sized
- + Copy
- + SampleUniform
- + PartialOrd
- + Add<Output = Self>
- + AddAssign
- + Sub<Output = Self>
- + SubAssign
- + Mul<Output = Self>
- + MulAssign
- + Div<Output = Self>
- + DivAssign
- + Sum
-{
- /// Maximum number representable by `Self`.
- const MAX: Self;
-
- /// Element of `Self` equivalent to 0.
- const ZERO: Self;
-
- /// Produce an instance of `Self` from a `u32` value, or return `None` if
- /// out of range. Loss of precision (where `Self` is a floating point type)
- /// is acceptable.
- fn try_from_u32_lossy(n: u32) -> Option<Self>;
-
- /// Sums all values in slice `values`.
- fn sum(values: &[Self]) -> Self {
- values.iter().map(|x| *x).sum()
- }
-}
-
-macro_rules! impl_weight_for_float {
- ($T: ident) => {
- impl Weight for $T {
- const MAX: Self = ::core::$T::MAX;
- const ZERO: Self = 0.0;
-
- fn try_from_u32_lossy(n: u32) -> Option<Self> {
- Some(n as $T)
- }
-
- fn sum(values: &[Self]) -> Self {
- pairwise_sum(values)
- }
- }
- };
-}
-
-/// In comparison to naive accumulation, the pairwise sum algorithm reduces
-/// rounding errors when there are many floating point values.
-fn pairwise_sum<T: Weight>(values: &[T]) -> T {
- if values.len() <= 32 {
- values.iter().map(|x| *x).sum()
- } else {
- let mid = values.len() / 2;
- let (a, b) = values.split_at(mid);
- pairwise_sum(a) + pairwise_sum(b)
- }
-}
-
-macro_rules! impl_weight_for_int {
- ($T: ident) => {
- impl Weight for $T {
- const MAX: Self = ::core::$T::MAX;
- const ZERO: Self = 0;
-
- fn try_from_u32_lossy(n: u32) -> Option<Self> {
- let n_converted = n as Self;
- if n_converted >= Self::ZERO && n_converted as u32 == n {
- Some(n_converted)
- } else {
- None
- }
- }
- }
- };
-}
-
-impl_weight_for_float!(f64);
-impl_weight_for_float!(f32);
-impl_weight_for_int!(usize);
-#[cfg(not(target_os = "emscripten"))]
-impl_weight_for_int!(u128);
-impl_weight_for_int!(u64);
-impl_weight_for_int!(u32);
-impl_weight_for_int!(u16);
-impl_weight_for_int!(u8);
-impl_weight_for_int!(isize);
-#[cfg(not(target_os = "emscripten"))]
-impl_weight_for_int!(i128);
-impl_weight_for_int!(i64);
-impl_weight_for_int!(i32);
-impl_weight_for_int!(i16);
-impl_weight_for_int!(i8);
-
-#[cfg(test)]
-mod test {
- use super::*;
-
- #[test]
- #[cfg(not(miri))] // Miri is too slow
- fn test_weighted_index_f32() {
- test_weighted_index(f32::into);
-
- // Floating point special cases
- assert_eq!(
- WeightedIndex::new(vec![::core::f32::INFINITY]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- assert_eq!(
- WeightedIndex::new(vec![-0_f32]).unwrap_err(),
- WeightedError::AllWeightsZero
- );
- assert_eq!(
- WeightedIndex::new(vec![-1_f32]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- assert_eq!(
- WeightedIndex::new(vec![-::core::f32::INFINITY]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- assert_eq!(
- WeightedIndex::new(vec![::core::f32::NAN]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- }
-
- #[cfg(not(target_os = "emscripten"))]
- #[test]
- #[cfg(not(miri))] // Miri is too slow
- fn test_weighted_index_u128() {
- test_weighted_index(|x: u128| x as f64);
- }
-
- #[cfg(all(rustc_1_26, not(target_os = "emscripten")))]
- #[test]
- #[cfg(not(miri))] // Miri is too slow
- fn test_weighted_index_i128() {
- test_weighted_index(|x: i128| x as f64);
-
- // Signed integer special cases
- assert_eq!(
- WeightedIndex::new(vec![-1_i128]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- assert_eq!(
- WeightedIndex::new(vec![::core::i128::MIN]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- }
-
- #[test]
- #[cfg(not(miri))] // Miri is too slow
- fn test_weighted_index_u8() {
- test_weighted_index(u8::into);
- }
-
- #[test]
- #[cfg(not(miri))] // Miri is too slow
- fn test_weighted_index_i8() {
- test_weighted_index(i8::into);
-
- // Signed integer special cases
- assert_eq!(
- WeightedIndex::new(vec![-1_i8]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- assert_eq!(
- WeightedIndex::new(vec![::core::i8::MIN]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- }
-
- fn test_weighted_index<W: Weight, F: Fn(W) -> f64>(w_to_f64: F)
- where
- WeightedIndex<W>: fmt::Debug,
- {
- const NUM_WEIGHTS: u32 = 10;
- const ZERO_WEIGHT_INDEX: u32 = 3;
- const NUM_SAMPLES: u32 = 15000;
- let mut rng = crate::test::rng(0x9c9fa0b0580a7031);
-
- let weights = {
- let mut weights = Vec::with_capacity(NUM_WEIGHTS as usize);
- let random_weight_distribution = crate::distributions::Uniform::new_inclusive(
- W::ZERO,
- W::MAX / W::try_from_u32_lossy(NUM_WEIGHTS).unwrap(),
- );
- for _ in 0..NUM_WEIGHTS {
- weights.push(rng.sample(&random_weight_distribution));
- }
- weights[ZERO_WEIGHT_INDEX as usize] = W::ZERO;
- weights
- };
- let weight_sum = weights.iter().map(|w| *w).sum::<W>();
- let expected_counts = weights
- .iter()
- .map(|&w| w_to_f64(w) / w_to_f64(weight_sum) * NUM_SAMPLES as f64)
- .collect::<Vec<f64>>();
- let weight_distribution = WeightedIndex::new(weights).unwrap();
-
- let mut counts = vec![0; NUM_WEIGHTS as usize];
- for _ in 0..NUM_SAMPLES {
- counts[rng.sample(&weight_distribution)] += 1;
- }
-
- assert_eq!(counts[ZERO_WEIGHT_INDEX as usize], 0);
- for (count, expected_count) in counts.into_iter().zip(expected_counts) {
- let difference = (count as f64 - expected_count).abs();
- let max_allowed_difference = NUM_SAMPLES as f64 / NUM_WEIGHTS as f64 * 0.1;
- assert!(difference <= max_allowed_difference);
- }
-
- assert_eq!(
- WeightedIndex::<W>::new(vec![]).unwrap_err(),
- WeightedError::NoItem
- );
- assert_eq!(
- WeightedIndex::new(vec![W::ZERO]).unwrap_err(),
- WeightedError::AllWeightsZero
- );
- assert_eq!(
- WeightedIndex::new(vec![W::MAX, W::MAX]).unwrap_err(),
- WeightedError::InvalidWeight
- );
- }
-}
diff --git a/rand/src/distributions/weighted/mod.rs b/rand/src/distributions/weighted/mod.rs
deleted file mode 100644
index 2711637..0000000
--- a/rand/src/distributions/weighted/mod.rs
+++ /dev/null
@@ -1,363 +0,0 @@
-// Copyright 2018 Developers of the Rand project.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Weighted index sampling
-//!
-//! This module provides two implementations for sampling indices:
-//!
-//! * [`WeightedIndex`] allows `O(log N)` sampling
-//! * [`alias_method::WeightedIndex`] allows `O(1)` sampling, but with
-//! much greater set-up cost
-//!
-//! [`alias_method::WeightedIndex`]: alias_method/struct.WeightedIndex.html
-
-pub mod alias_method;
-
-use crate::Rng;
-use crate::distributions::Distribution;
-use crate::distributions::uniform::{UniformSampler, SampleUniform, SampleBorrow};
-use core::cmp::PartialOrd;
-use core::fmt;
-
-// Note that this whole module is only imported if feature="alloc" is enabled.
-#[cfg(not(feature="std"))] use crate::alloc::vec::Vec;
-
-/// A distribution using weighted sampling to pick a discretely selected
-/// item.
-///
-/// Sampling a `WeightedIndex` distribution returns the index of a randomly
-/// selected element from the iterator used when the `WeightedIndex` was
-/// created. The chance of a given element being picked is proportional to the
-/// value of the element. The weights can use any type `X` for which an
-/// implementation of [`Uniform<X>`] exists.
-///
-/// # Performance
-///
-/// A `WeightedIndex<X>` contains a `Vec<X>` and a [`Uniform<X>`] and so its
-/// size is the sum of the size of those objects, possibly plus some alignment.
-///
-/// Creating a `WeightedIndex<X>` will allocate enough space to hold `N - 1`
-/// weights of type `X`, where `N` is the number of weights. However, since
-/// `Vec` doesn't guarantee a particular growth strategy, additional memory
-/// might be allocated but not used. Since the `WeightedIndex` object also
-/// contains, this might cause additional allocations, though for primitive
-/// types, ['Uniform<X>`] doesn't allocate any memory.
-///
-/// Time complexity of sampling from `WeightedIndex` is `O(log N)` where
-/// `N` is the number of weights.
-///
-/// Sampling from `WeightedIndex` will result in a single call to
-/// `Uniform<X>::sample` (method of the [`Distribution`] trait), which typically
-/// will request a single value from the underlying [`RngCore`], though the
-/// exact number depends on the implementaiton of `Uniform<X>::sample`.
-///
-/// # Example
-///
-/// ```
-/// use rand::prelude::*;
-/// use rand::distributions::WeightedIndex;
-///
-/// let choices = ['a', 'b', 'c'];
-/// let weights = [2, 1, 1];
-/// let dist = WeightedIndex::new(&weights).unwrap();
-/// let mut rng = thread_rng();
-/// for _ in 0..100 {
-/// // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
-/// println!("{}", choices[dist.sample(&mut rng)]);
-/// }
-///
-/// let items = [('a', 0), ('b', 3), ('c', 7)];
-/// let dist2 = WeightedIndex::new(items.iter().map(|item| item.1)).unwrap();
-/// for _ in 0..100 {
-/// // 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
-/// println!("{}", items[dist2.sample(&mut rng)].0);
-/// }
-/// ```
-///
-/// [`Uniform<X>`]: crate::distributions::uniform::Uniform
-/// [`RngCore`]: crate::RngCore
-#[derive(Debug, Clone)]
-pub struct WeightedIndex<X: SampleUniform + PartialOrd> {
- cumulative_weights: Vec<X>,
- total_weight: X,
- weight_distribution: X::Sampler,
-}
-
-impl<X: SampleUniform + PartialOrd> WeightedIndex<X> {
- /// Creates a new a `WeightedIndex` [`Distribution`] using the values
- /// in `weights`. The weights can use any type `X` for which an
- /// implementation of [`Uniform<X>`] exists.
- ///
- /// Returns an error if the iterator is empty, if any weight is `< 0`, or
- /// if its total value is 0.
- ///
- /// [`Uniform<X>`]: crate::distributions::uniform::Uniform
- pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>
- where I: IntoIterator,
- I::Item: SampleBorrow<X>,
- X: for<'a> ::core::ops::AddAssign<&'a X> +
- Clone +
- Default {
- let mut iter = weights.into_iter();
- let mut total_weight: X = iter.next()
- .ok_or(WeightedError::NoItem)?
- .borrow()
- .clone();
-
- let zero = <X as Default>::default();
- if total_weight < zero {
- return Err(WeightedError::InvalidWeight);
- }
-
- let mut weights = Vec::<X>::with_capacity(iter.size_hint().0);
- for w in iter {
- if *w.borrow() < zero {
- return Err(WeightedError::InvalidWeight);
- }
- weights.push(total_weight.clone());
- total_weight += w.borrow();
- }
-
- if total_weight == zero {
- return Err(WeightedError::AllWeightsZero);
- }
- let distr = X::Sampler::new(zero, total_weight.clone());
-
- Ok(WeightedIndex { cumulative_weights: weights, total_weight, weight_distribution: distr })
- }
-
- /// Update a subset of weights, without changing the number of weights.
- ///
- /// `new_weights` must be sorted by the index.
- ///
- /// Using this method instead of `new` might be more efficient if only a small number of
- /// weights is modified. No allocations are performed, unless the weight type `X` uses
- /// allocation internally.
- ///
- /// In case of error, `self` is not modified.
- pub fn update_weights(&mut self, new_weights: &[(usize, &X)]) -> Result<(), WeightedError>
- where X: for<'a> ::core::ops::AddAssign<&'a X> +
- for<'a> ::core::ops::SubAssign<&'a X> +
- Clone +
- Default {
- if new_weights.is_empty() {
- return Ok(());
- }
-
- let zero = <X as Default>::default();
-
- let mut total_weight = self.total_weight.clone();
-
- // Check for errors first, so we don't modify `self` in case something
- // goes wrong.
- let mut prev_i = None;
- for &(i, w) in new_weights {
- if let Some(old_i) = prev_i {
- if old_i >= i {
- return Err(WeightedError::InvalidWeight);
- }
- }
- if *w < zero {
- return Err(WeightedError::InvalidWeight);
- }
- if i >= self.cumulative_weights.len() + 1 {
- return Err(WeightedError::TooMany);
- }
-
- let mut old_w = if i < self.cumulative_weights.len() {
- self.cumulative_weights[i].clone()
- } else {
- self.total_weight.clone()
- };
- if i > 0 {
- old_w -= &self.cumulative_weights[i - 1];
- }
-
- total_weight -= &old_w;
- total_weight += w;
- prev_i = Some(i);
- }
- if total_weight == zero {
- return Err(WeightedError::AllWeightsZero);
- }
-
- // Update the weights. Because we checked all the preconditions in the
- // previous loop, this should never panic.
- let mut iter = new_weights.iter();
-
- let mut prev_weight = zero.clone();
- let mut next_new_weight = iter.next();
- let &(first_new_index, _) = next_new_weight.unwrap();
- let mut cumulative_weight = if first_new_index > 0 {
- self.cumulative_weights[first_new_index - 1].clone()
- } else {
- zero.clone()
- };
- for i in first_new_index..self.cumulative_weights.len() {
- match next_new_weight {
- Some(&(j, w)) if i == j => {
- cumulative_weight += w;
- next_new_weight = iter.next();
- },
- _ => {
- let mut tmp = self.cumulative_weights[i].clone();
- tmp -= &prev_weight; // We know this is positive.
- cumulative_weight += &tmp;
- }
- }
- prev_weight = cumulative_weight.clone();
- core::mem::swap(&mut prev_weight, &mut self.cumulative_weights[i]);
- }
-
- self.total_weight = total_weight;
- self.weight_distribution = X::Sampler::new(zero, self.total_weight.clone());
-
- Ok(())
- }
-}
-
-impl<X> Distribution<usize> for WeightedIndex<X> where
- X: SampleUniform + PartialOrd {
- fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
- use ::core::cmp::Ordering;
- let chosen_weight = self.weight_distribution.sample(rng);
- // Find the first item which has a weight *higher* than the chosen weight.
- self.cumulative_weights.binary_search_by(
- |w| if *w <= chosen_weight { Ordering::Less } else { Ordering::Greater }).unwrap_err()
- }
-}
-
-#[cfg(test)]
-mod test {
- use super::*;
-
- #[test]
- #[cfg(not(miri))] // Miri is too slow
- fn test_weightedindex() {
- let mut r = crate::test::rng(700);
- const N_REPS: u32 = 5000;
- let weights = [1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7];
- let total_weight = weights.iter().sum::<u32>() as f32;
-
- let verify = |result: [i32; 14]| {
- for (i, count) in result.iter().enumerate() {
- let exp = (weights[i] * N_REPS) as f32 / total_weight;
- let mut err = (*count as f32 - exp).abs();
- if err != 0.0 {
- err /= exp;
- }
- assert!(err <= 0.25);
- }
- };
-
- // WeightedIndex from vec
- let mut chosen = [0i32; 14];
- let distr = WeightedIndex::new(weights.to_vec()).unwrap();
- for _ in 0..N_REPS {
- chosen[distr.sample(&mut r)] += 1;
- }
- verify(chosen);
-
- // WeightedIndex from slice
- chosen = [0i32; 14];
- let distr = WeightedIndex::new(&weights[..]).unwrap();
- for _ in 0..N_REPS {
- chosen[distr.sample(&mut r)] += 1;
- }
- verify(chosen);
-
- // WeightedIndex from iterator
- chosen = [0i32; 14];
- let distr = WeightedIndex::new(weights.iter()).unwrap();
- for _ in 0..N_REPS {
- chosen[distr.sample(&mut r)] += 1;
- }
- verify(chosen);
-
- for _ in 0..5 {
- assert_eq!(WeightedIndex::new(&[0, 1]).unwrap().sample(&mut r), 1);
- assert_eq!(WeightedIndex::new(&[1, 0]).unwrap().sample(&mut r), 0);
- assert_eq!(WeightedIndex::new(&[0, 0, 0, 0, 10, 0]).unwrap().sample(&mut r), 4);
- }
-
- assert_eq!(WeightedIndex::new(&[10][0..0]).unwrap_err(), WeightedError::NoItem);
- assert_eq!(WeightedIndex::new(&[0]).unwrap_err(), WeightedError::AllWeightsZero);
- assert_eq!(WeightedIndex::new(&[10, 20, -1, 30]).unwrap_err(), WeightedError::InvalidWeight);
- assert_eq!(WeightedIndex::new(&[-10, 20, 1, 30]).unwrap_err(), WeightedError::InvalidWeight);
- assert_eq!(WeightedIndex::new(&[-10]).unwrap_err(), WeightedError::InvalidWeight);
- }
-
- #[test]
- fn test_update_weights() {
- let data = [
- (&[10u32, 2, 3, 4][..],
- &[(1, &100), (2, &4)][..], // positive change
- &[10, 100, 4, 4][..]),
- (&[1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7][..],
- &[(2, &1), (5, &1), (13, &100)][..], // negative change and last element
- &[1u32, 2, 1, 0, 5, 1, 7, 1, 2, 3, 4, 5, 6, 100][..]),
- ];
-
- for (weights, update, expected_weights) in data.into_iter() {
- let total_weight = weights.iter().sum::<u32>();
- let mut distr = WeightedIndex::new(weights.to_vec()).unwrap();
- assert_eq!(distr.total_weight, total_weight);
-
- distr.update_weights(update).unwrap();
- let expected_total_weight = expected_weights.iter().sum::<u32>();
- let expected_distr = WeightedIndex::new(expected_weights.to_vec()).unwrap();
- assert_eq!(distr.total_weight, expected_total_weight);
- assert_eq!(distr.total_weight, expected_distr.total_weight);
- assert_eq!(distr.cumulative_weights, expected_distr.cumulative_weights);
- }
- }
-}
-
-/// Error type returned from `WeightedIndex::new`.
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum WeightedError {
- /// The provided weight collection contains no items.
- NoItem,
-
- /// A weight is either less than zero, greater than the supported maximum or
- /// otherwise invalid.
- InvalidWeight,
-
- /// All items in the provided weight collection are zero.
- AllWeightsZero,
-
- /// Too many weights are provided (length greater than `u32::MAX`)
- TooMany,
-}
-
-impl WeightedError {
- fn msg(&self) -> &str {
- match *self {
- WeightedError::NoItem => "No weights provided.",
- WeightedError::InvalidWeight => "A weight is invalid.",
- WeightedError::AllWeightsZero => "All weights are zero.",
- WeightedError::TooMany => "Too many weights (hit u32::MAX)",
- }
- }
-}
-
-#[cfg(feature="std")]
-impl ::std::error::Error for WeightedError {
- fn description(&self) -> &str {
- self.msg()
- }
- fn cause(&self) -> Option<&dyn (::std::error::Error)> {
- None
- }
-}
-
-impl fmt::Display for WeightedError {
- fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
- write!(f, "{}", self.msg())
- }
-}
generated by cgit v1.2.1 (git 2.18.0) at 2024-05-17 08:19:27 +0000